3GPP TR 38.901 V19.1.0 (2025-09) | |
Technical Report
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3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Study on channel model for frequencies from 0.5 to 100 GHz (Release 19) | |
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The present document has been developed within the 3rd Generation Partnership Project (3GPP TM) and may be further elaborated for the purposes of 3GPP.The present document has not been subject to any approval process by the 3GPP<br> Organizational Partners and shall not be implemented.This Specification is provided for future development work within 3GPP<br> only. The Organizational Partners accept no liability for any use of this Specification.Specifications and Reports for implementation of the 3GPP<br> TM system should be obtained via the 3GPP Organizational Partners' Publications Offices.
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Contents #
1 Scope 8
2 References 8
3 Definitions, symbols and abbreviations 9
3.1 Definitions 9
3.2 Symbols 9
3.3 Abbreviations 10
4 Introduction 12
5 Void 13
6 Status/expectation of existing information on high frequencies 13
6.1 Channel modelling works outside of 3GPP 13
6.2 Scenarios of interest 15
6.3 Channel measurement capabilities 16
6.4 Modelling objectives 18
7 Channel model(s) for 0.5-100 GHz 18
7.1 Coordinate system 18
7.1.1 Definition 18
7.1.2 Local and global coordinate systems 19
7.1.3 Transformation from a LCS to a GCS 19
7.1.4 Transformation from an LCS to a GCS for downtilt angle only 23
7.2 Scenarios 24
7.3 Antenna modelling 28
7.3.0 Antenna array structure 28
7.3.1 Antenna port mapping 32
7.3.2 Polarized antenna modelling 32
7.4 Pathloss, LOS probability and penetration modelling 35
7.4.1 Pathloss 35
7.4.2 LOS probability 38
7.4.3 O2I penetration loss 39
7.4.3.1 O2I building penetration loss 39
7.4.3.2 O2I car penetration loss 41
7.4.4 Autocorrelation of shadow fading 41
7.5 Fast fading model 41
7.6 Additional modelling components 58
7.6.0 Introduction of additional modelling components 58
7.6.1 Oxygen absorption 58
7.6.2 Large bandwidth and large antenna array 59
7.6.2.1 Modelling of the propagation delay 59
7.6.2.2 Modelling of intra-cluster angular and delay spreads 60
7.6.3 Spatial consistency 61
7.6.3.1 Spatial consistency procedure 61
7.6.3.2 Spatially-consistent UT/BS mobility modelling 62
7.6.3.3 LOS/NLOS, indoor states and O2I parameters 66
7.6.3.4 Applicability of spatial consistency 67
7.6.4 Blockage 67
7.6.4.1 Blockage model A 68
7.6.4.2 Blockage model B 70
7.6.5 Correlation modelling for multi-frequency simulations 72
7.6.5.1 Alternative channel generation method 73
7.6.6 Time-varying Doppler shift 75
7.6.7 UT rotation 75
7.6.8 Explicit ground reflection model 75
7.6.9 Absolute time of arrival 78
7.6.10 Dual mobility 79
7.6.11 Sources of EM interference 79
7.6.12 Embedded devices 79
7.6.13 Near-field channel model 80
7.6.14 Spatial non-stationarity channel model 81
7.6.14.1 Spatial non-stationarity channel model at TRP side 81
7.6.14.1.1 Introduction 81
7.6.14.1.2 Physical blocker-based Model 82
7.6.14.1.3 Stochastic based Model 83
7.6.14.2 Spatial non-stationarity channel model at UT side 84
7.6.15 Number of cluster variability model 85
7.6.16 Polarization power variability model 85
7.7 Channel models for link-level evaluations 86
7.7.1 Clustered Delay Line (CDL) models 86
7.7.2 Tapped Delay Line (TDL) models 90
7.7.3 Scaling of delays 93
7.7.4 Spatial filter for generating TDL channel model 94
7.7.4.1 Exemplary filters/antenna patterns 94
7.7.4.2 Generation procedure 95
7.7.5 Extension for MIMO simulations 95
7.7.5.1 CDL extension: Scaling of angles 95
7.7.5.2 TDL extension: Applying a correlation matrix 97
7.7.6 K-factor for LOS channel models 97
7.8 Channel model calibration 98
7.8.1 Large scale calibration 98
7.8.2 Full calibration 100
7.8.3 Calibration of additional features 102
7.8.4 Calibration of the indoor factory scenario 110
7.9 Channel model(s) for ISAC 111
7.9.0 Introduction 111
7.9.1 Scenarios 112
7.9.2 Physical object model 117
7.9.2.0 Introduction 117
7.9.2.1 RCS of a sensing target 117
7.9.2.2 Cross-polarization matrix of a sensing target 119
7.9.3 Reference channel models and required updates 120
7.9.4 Fast fading model 125
7.9.4.0 Introduction 125
7.9.4.1 Target channel 127
7.9.4.2 Background channel 132
7.9.4.3 Combining target channel and background channel 134
7.9.5 Additional modelling components 134
7.9.5.0 Introduction 134
7.9.5.1 Spatial consistency 134
7.9.5.2 Type-2 environment object 135
7.9.5.3 Power normalization across target channel and background channel 139
7.9.5.4 Doppler of mobile scatterers 139
7.9.5.5 Lower power clusters 139
7.9.5.6 Blockage 140
7.9.6 Channel model calibration 140
7.9.6.1 Large scale calibration 140
7.9.6.2 Full calibration 144
7.9.6.3 Calibration of additional features 145
8 Map-based hybrid channel model (Alternative channel model methodology) 149
8.1 Coordinate system 149
8.2 Scenarios 149
8.3 Antenna modelling 149
8.4 Channel generation 149
Annex A: Further parameter definitions 160
A.1 Calculation of angular spread 160
A.2 Calculation of mean angle 160
A.3 Calculation of cluster angular spread 160
A.4 Calculation of cluster mean angle 160
A.5 Calculation of scaling factor for changing CDL model angular spread 160
Annex B: Change history 162
<br>Foreword #
This Technical Report has been produced by the 3rd Generation Partnership Project (3GPP).
The contents of the present document are subject to continuing work within the TSG and may change following formal TSG approval. Should the TSG modify the contents of the present document, it will be re-released by the TSG with an identifying change of release date and an increase in version number as follows:
Version x.y.z
where:
x the first digit:
1 presented to TSG for information;
2 presented to TSG for approval;
3 or greater indicates TSG approved document under change control.
y the second digit is incremented for all changes of substance, i.e. technical enhancements, corrections, updates, etc.
z the third digit is incremented when editorial only changes have been incorporated in the document.
In the present document, modal verbs have the following meanings:
shall indicates a mandatory requirement to do something
shall not indicates an interdiction (prohibition) to do something
The constructions "shall" and "shall not" are confined to the context of normative provisions, and do not appear in Technical Reports.
The constructions "must" and "must not" are not used as substitutes for "shall" and "shall not". Their use is avoided insofar as possible, and they are not used in a normative context except in a direct citation from an external, referenced, non-3GPP document, or so as to maintain continuity of style when extending or modifying the provisions of such a referenced document.
should indicates a recommendation to do something
should not indicates a recommendation not to do something
may indicates permission to do something
need not indicates permission not to do something
The construction "may not" is ambiguous and is not used in normative elements. The unambiguous constructions "might not" or "shall not" are used instead, depending upon the meaning intended.
can indicates that something is possible
cannot indicates that something is impossible
The constructions "can" and "cannot" are not substitutes for "may" and "need not".
will indicates that something is certain or expected to happen as a result of action taken by an agency the behaviour of which is outside the scope of the present document
will not indicates that something is certain or expected not to happen as a result of action taken by an agency the behaviour of which is outside the scope of the present document
might indicates a likelihood that something will happen as a result of action taken by some agency the behaviour of which is outside the scope of the present document
might not indicates a likelihood that something will not happen as a result of action taken by some agency the behaviour of which is outside the scope of the present document
In addition:
is (or any other verb in the indicative mood) indicates a statement of fact
is not (or any other negative verb in the indicative mood) indicates a statement of fact
The constructions "is" and "is not" do not indicate requirements.
<br>1 Scope #
The present document captures the findings of the study item, "Study on channel model for frequency spectrum above 6 GHz" [2] and from further findings of:
- the study item, "Study on New Radio Access Technology [22]",
- the study item "Study on Channel Modeling for Indoor Industrial Scenarios [23]",
- the study item "New SID: Study on channel modelling enhancements for 7-24GHz for NR [24]",
- and the study item "Study on channel modelling for Integrated Sensing And Communication (ISAC) for NR [26]".
The channel models in the present document address the frequency range 0.5-100 GHz. The purpose of this TR is to help TSG RAN WG1 to properly model and evaluate the performance of physical layer techniques using the appropriate channel model(s). Therefore, the TR will be kept up-to-date via CRs in the future.
This document relates to the 3GPP evaluation methodology and covers the modelling of the physical layer of both Mobile Equipment and Access Network of 3GPP systems.
This document is intended to capture the channel model(s) for frequencies from 0.5GHz up to 100GHz.
2 References #
The following documents contain provisions which, through reference in this text, constitute provisions of the present document.
- References are either specific (identified by date of publication, edition number, version number, etc.) or nonspecific.
- For a specific reference, subsequent revisions do not apply.
- For a non-specific reference, the latest version applies. In the case of a reference to a 3GPP document (including a GSM document), a non-specific reference implicitly refers to the latest version of that document in the same Release as the present document.
[1] 3GPP TR 21.905: "Vocabulary for 3GPP Specifications".
[2] 3GPP TD RP-151606: "Study on channel model for frequency spectrum above 6 GHz".
[3] 3GPP TR 36.873 (V12.2.0): "Study on 3D channel model for LTE".
[4] 3GPP RP-151847: "Report of RAN email discussion about >6GHz channel modelling", Samsung.
[5] 3GPP TD R1-163408: "Additional Considerations on Building Penetration Loss Modelling for 5G System Performance Evaluation", Straight Path Communications.
[6] ICT-317669-METIS/D1.4: "METIS channel model, METIS 2020, Feb, 2015".
[7] Glassner, A S: "An introduction to ray tracing. Elsevier, 1989".
[8] McKown, J. W., Hamilton, R. L.: "Ray tracing as a design tool for radio networks, Network, IEEE, 1991(6): 27-30".
[9] Kurner, T., Cichon, D. J., Wiesbeck, W.: "Concepts and results for 3D digital terrain-based wave propagation models: An overview", IEEE J.Select. Areas Commun., vol. 11, pp. 1002–1012, 1993.
[10] Born, M., Wolf, E.: "Principles of optics: electromagnetic theory of propagation, interference and diffraction of light", CUP Archive, 2000.
[11] Friis, H.: "A note on a simple transmission formula", proc. IRE, vol. 34, no. 5, pp. 254–256, 1946.
[12] Kouyoumjian, R.G., Pathak, P.H.: "A uniform geometrical theory of diffraction for an edge in a perfectly conducting surface" Proc. IEEE, vol. 62, pp. 1448–1461, Nov. 1974.
[13] Pathak, P.H., Burnside, W., Marhefka, R.: "A Uniform GTD Analysis of the Diffraction of Electromagnetic Waves by a Smooth Convex Surface", IEEE Transactions on Antennas and Propagation, vol. 28, no. 5, pp. 631–642, 1980.
[14] IST-WINNER II Deliverable 1.1.2 v.1.2, "WINNER II Channel Models", IST-WINNER2, Tech. Rep., 2007 (http://www.ist-winner.org/deliverables.html).
[15] 3GPP TR36.101: "User Equipment (UE) radio transmission and reception".
[16] 3GPP TR36.104: "Base Station (BS) radio transmission and reception".
[17] Asplund, H., Medbo, J., Göransson, B., Karlsson, J., Sköld, J.: "A simplified approach to applying the 3GPP spatial channel model", in Proc. of PIMRC 2006.
[18] ITU-R Rec. P.1816: "The prediction of the time and the spatial profile for broadband land mobile services using UHF and SHF bands".
[19] ITU-R Rec. P.2040-1: "Effects of building materials and structures on radiowave propagation above about 100 MHz", International Telecommunication Union Radiocommunication Sector ITU-R, 07/2015.
[20] ITU-R Rec. P.527-3: "Electrical characteristics of the surface of the earth", International Telecommunication Union Radiocommunication Sector ITU-R, 03/1992.
[21] Jordan, E.C., Balmain, K.G.: "Electromagnetic Waves and Radiating Systems", Prentice-Hall Inc., 1968.
[22] 3GPP TD RP-162469: "Study on New Radio (NR) Access Technology".
[23] 3GPP TD RP-182138: "SID on Channel Modeling for Indoor Industrial Scenarios".
[24] 3GPP TD RP-234018: "New SID: Study on channel modelling enhancements for 7-24GHz for NR"
[25] 3GPP TD R1-2504960: "Data source descriptions for 7 – 24 GHz SI".
[26] 3GPP TD RP-242348: "Study on channel modelling for Integrated Sensing And Communication (ISAC) for NR".
[ 27] 3GPP TD R1-2504948: "Information on validations for ISAC"
3 Definitions, symbols and abbreviations #
3.1 Definitions #
For the purposes of the present document, the terms and definitions given in TR 21.905 [1] apply.
3.2 Symbols #
For the purposes of the present document, the following symbols apply:
antenna radiation power pattern
maximum attenuation
d2D 2D distance between Tx and Rx
d3D 3D distance between Tx and Rx
antenna element spacing in horizontal direction
antenna element spacing in vertical direction
f frequency
fc center frequency / carrier frequency
Frx,u,θ Receive antenna element u field pattern in the direction of the spherical basis vector 
Frx,u,ϕ Receive antenna element u field pattern in the direction of the spherical basis vector 
Ftx,s,θ Transmit antenna element s field pattern in the direction of the spherical basis vector
Frx,s,ϕ Transmit antenna element s field pattern in the direction of the spherical basis vector
hBS antenna height for BS
hUT antenna height for UT
spherical unit vector of cluster n, ray m, for receiver
spherical unit vector of cluster n, ray m, for transmitter
α bearing angle
β downtilt angle
γ slant angle
wavelength
κ cross-polarization power ratio in linear scale
μlgASA mean value of 10-base logarithm of azimuth angle spread of arrival
μlgASD mean value of 10-base logarithm of azimuth angle spread of departure
μlgDS mean value of 10-base logarithm of delay spread
μlgZSA mean value of 10-base logarithm of zenith angle spread of arrival
μlgZSD mean value of 10-base logarithm of zenith angle spread of departure
LOS probability
side-lobe attenuation in vertical direction
σlgASA standard deviation of 10-base logarithm of azimuth angle spread of arrival
σlgASD standard deviation of 10-base logarithm of azimuth angle spread of departure
σlgDS standard deviation value of 10-base logarithm of delay spread
σlgZSA standard deviation of 10-base logarithm of zenith angle spread of arrival
σlgZSD standard deviation of 10-base logarithm of zenith angle spread of departure
standard deviation of SF
azimuth angle
zenith angle
spherical basis vector (unit vector) for GCS
spherical basis vector (unit vector) for LCS
horizontal 3 dB beamwidth of an antenna
spherical basis vector (unit vector), orthogonal to , for GCS
spherical basis vector (unit vector), orthogonal to , for LCS
electrical steering angle in vertical direction
vertical 3 dB beamwidth of an antenna
ψ Angular displacement between two pairs of unit vectors
3.3 Abbreviations #
For the purposes of the present document, the abbreviations given in TR 21.905 [1] and the following apply. An abbreviation defined in the present document takes precedence over the definition of the same abbreviation, if any, in TR 21.905 [1].
2D two-dimensional
3D three-dimensional
AGV Automated Guided Vehicles
AOA Azimuth angle Of Arrival
AOD Azimuth angle Of Departure
AS Angular Spread
ASA Azimuth angle Spread of Arrival
ASD Azimuth angle Spread of Departure
BF Beamforming
BS Base Station
BP Breakpoint
BW Beamwidth
CDF Cumulative Distribution Function
CDL Clustered Delay Line
CRS Common Reference Signal
D2D Device-to-Device
DFT Discrete Fourier Transform
DS Delay Spread
EO Environment Object
GCS Global Coordinate System
IID Independent and identically distributed
InF Indoor Factory
InF-SL Indoor Factory with Sparse clutter and Low base station height (both Tx and Rx are below the average height of the clutter)
InF-DL Indoor Factory with Dense clutter and Low base station height (both Tx and Rx are below the average height of the clutter)
InF-SH Indoor Factory with Sparse clutter and High base station height (Tx or Rx elevated above the clutter)
InF-DH Indoor Factory with Dense clutter and High base station height (Tx or Rx elevated above the clutter)
InF-HH Indoor Factory with High Tx and High Rx (both elevated above the clutter)
InH Indoor Hotspot
IRR Infrared Reflecting
ISAC Integrated Sensing and Communication
ISD Intersite Distance
K Ricean K factor
LCS Local Coordinate System
LOS Line Of Sight
MIMO Multiple-Input-Multiple-Output
MPC Multipath Component
NLOS Non-LOS
O2I Outdoor-to-Indoor
O2O Outdoor-to-Outdoor
OFDM Orthogonal Frequency-Division Multiplexing
PAS Power angular spectrum
PL Path Loss
PRB Physical Resource Block
RCS Radar cross-section
RMa Rural Macro
RMS Root Mean Square
RP Reference Point
RSRP Reference Signal Received Power
Rx Receiver
SCM Spatial Channel Model
SINR Signal-to-Interference-plus-Noise Ratio
SIR Signal-to-Interference Ratio
SSCM Statistical Spatial Channel Model
SF Shadow Fading
SLA Sidelobe Attenuation
SPST Scattering Point of a ST
SRX Sensing Receiver
ST Sensing Target
STX Sensing Transmitter
TDL Tapped Delay Line
TOA Time Of Arrival
TRP Transmission Reception Point
Tx Transmitter
UAV Unmanned Aerial Vehicle
UMa Urban Macro
UMi Urban Micro
UT User Terminal
UTD Uniform Theory of Diffraction
V2V Vehicle-to-Vehicle
XPR Cross-Polarization Ratio
ZOA Zenith angle Of Arrival
ZOD Zenith angle Of Departure
ZSA Zenith angle Spread of Arrival
ZSD Zenith angle Spread of Departure
4 Introduction #
At TSG RAN #69 meeting the Study Item Description on "Study on channel model for frequency spectrum above 6 GHz" was approved [2]. This study item covers the identification of the status/expectation of existing information on high frequencies (e.g. spectrum allocation, scenarios of interest, measurements, etc), and the channel model(s) for frequencies up to 100 GHz. This technical report documents the channel model(s). The new channel model has to a large degree been aligned with earlier channel models for <6 GHz such as the 3D SCM model (TR 36.873) or IMT-Advanced (ITU-R M.2135). The new model supports comparisons across frequency bands over the range 0.5-100 GHz. The modelling methods defined in this technical report are generally applicable over the range 0.5-100 GHz, unless explicitly mentioned otherwise in this technical report for specific modelling method, involved parameters and/or scenario.
Subsequently, at the TSG RAN #81 meeting the Study Item Description "Study on Channel Modeling for Indoor Industrial Scenarios" was approved [23]. The findings from this study item is also captured in the present technical report. The Industrial channel model was developed by considering new measurements and information in the literature. An overview list of all such contributions and sources is available in tdoc R1-1909706.
Subsequently, at the TSG RAN #102 meeting the Study Item Description "Study on channel modelling enhancements for 7-24GHz for NR" was approved [24]. The findings from this study are also captured in the present technical report. Channel modeling was further updated by considering new measurements and information in the literature. An overview of the information sources is available in tdoc R1-2504960 [25].
At TSG RAN #102 meeting the Study Item Description “Study on channel modelling for Integrated Sensing And Communication (ISAC) for NR” was approved. The findings from this study item are captured in Clause 7.9. The ISAC channel model was developed considering new measurements and information in the literature. The RCS validation results are obtained based on certain object sizes. An overview list of the sources is available in [27].
The channel model is applicable for link and system level simulations in the following conditions:
- For system level simulations, supported scenarios are urban microcell street canyon, urban macrocell, indoor office, rural macrocell, indoor factory, and suburban macrocell.
- Bandwidth is supported up to 10% of the center frequency but no larger than 2GHz.
- Mobility of either one end of the link or both ends of the link is supported
- For the stochastic model, spatial consistency is supported by correlation of LSPs and SSPs as well as LOS/NLOS state.
- Large array support is based on far field assumption and stationary channel over the size of the array.
- Near field channel modeling (i.e., characteristics of spherical wavefront) and spatial non-stationarity (i.e., antenna element-wise power variation) are supported.
- The sensing target is assumed in the far field of the sensing transmitter/receiver for the channel model(s) for ISAC in Clause 7.9.
5 Void #
6 Status/expectation of existing information on high frequencies #
6.1 Channel modelling works outside of 3GPP #
This clause summarizes the channel modelling work outside of 3GPP based on the input from companies.
Groups and projects with channel models:
- METIS (Mobile and wireless communications Enablers for the Twenty-twenty Information Society)
- MiWEBA (Millimetre-Wave Evolution for Backhaul and Access)
- ITU-R M
- COST2100
- IEEE 802.11
- NYU WIRELESS: interdisciplinary academic research center
- Fraunhofer HHI has developed the QuaDRiGa channel model, Matlab implementation is available at http://quadriga-channel-model.de
Groups and projects which intend to develop channel models:
- 5G mmWave Channel Model Alliance: NIST initiated, North America based
- mmMAGIC (Millimetre-Wave Based Mobile Radio Access Network for Fifth Generation Integrated Communications): Europe based
- IMT-2020 5G promotion association: China based
METIS Channel Models:
- Identified 5G requirements (e.g., wide frequency range, high bandwidth, massive MIMO, 3-D and accurate polarization modelling)
- Performed channel measurements at various bands between 2GHz and 60 GHz
- Provided different channel model methodologies (map-based model, stochastic model or hybrid model). For stochastic model, the proposed channel is focused on outdoor square, Indoor cafeteria and indoor shopping mall scenarios.
MiWEBA Channel Models:
- Addressed various challenges: Shadowing, spatial consistency, environment dynamics, spherical wave modelling, dual mobility Doppler model, ratio between diffuse and specular reflections, polarization
- Proposed Quasi-deterministic channel model
- Performed channel measurements at 60 GHz
- Focused on university campus, street canyon, hotel lobby, backhaul, and D2D scenarios.
ITU-R M Channel Models:
- Addressed the propagation loss and atmospheric loss on mmW
- Introduced enabling antenna array technology and semiconductor technology
- Proposed deployment scenarios, focused on dense urban environment for high data rate service: indoor shopping mall, indoor enterprise, in home, urban hotspot in a square/street, mobility in city.
COST2100 and COST IC1004 Channel Models:
- Geometry-based stochastic channel model that reproduce the stochastic properties of MIMO channels over time, frequency and space. It is a cluster-level model where the statistics of the large scale parameters are always guaranteed in each series of channel instances.
NYU WIRELESS Channel Models:
- Conducted many urban propagation measurements on 28/38/60/73 GHz bands for both outdoor and indoor channels, measurements are continuing.
- Proposed 3 areas for 5G mmWave channel modelling which are small modifications or extensions from 3GPP's current below 6GHz channel models
- 1) LOS/NLOS/blockage modelling (a squared exponential term); 2). Wideband power delay profiles (time clusters and spatial lobes for a simple extension to the existing 3GPP SSCM model); 3). Physics-based path loss model (using the existing 3GPP path loss equations, but simply replacing the "floating" optimization parameter with a deterministic 1 m "close-in" free space reference term in order to provide a standard and stable definition of "path loss exponent" across all different parties, scenarios, and frequencies).
802.11 ad/ay Channel Models:
- Conducted ray-tracing methodology on 60 GHz band indoor channels, including conference room, cubicle, living room scenarios
- Intra cluster parameters were proposed in terms of ray excess delay and ray power distribution
- Human blockage models were proposed in terms of blockage probability and blockage attenuation
5G mmWave Channel Model Alliance:
- Will provide a venue to promote fundamental research into measurement, analysis, identification of physical parameters, and statistical representations of mmWave propagation channels.
- Divided into six collaborative working groups that include a Steering Committee; Modelling Methodology Group; Measurement Methodology Group; and groups that focus on defining and parameterizing Indoor, Outdoor, and Emerging Usage Scenarios.
- Sponsored by Communications Technology Research Laboratory within the NIST.
mmMAGIC:
- Brings together major infrastructure vendors, major European operators, leading research institutes and universities, measurement equipment vendors and one SME.
- Will undertake extensive radio channel measurements in the 6-100 GHz range.
- Will develop and validate advanced channel models that will be used for rigorous validation and feasibility analysis of the proposed concepts and system, as well as for usage in regulatory and standards fora.
IMT-2020 5G promotion association
- Jointly established by three ministries of China based on the original IMT-Advanced promotion group
- Members including the main operators, vendors, universities and research institutes in China
- The major platform to promote 5G technology research in China and to facilitate international communication and cooperation
QuaDRiGa (Fraunhofer HHI)
- QuaDRiGa (QUAsi Deterministic RadIo channel GenerAtor) was developed at the Fraunhofer Heinrich Hertz Institute within the Wireless Communications and Networks Department to enable the modelling of MIMO radio channels for specific network configurations, such as indoor, satellite or heterogeneous configurations.
- Besides being a fully-fledged 3D geometry-based stochastic channel model (well aligned with TR36.873), QuaDRiGa contains a collection of features created in SCM(e) and WINNER channel models along with novel modelling approaches which provide features to enable quasi-deterministic multi-link tracking of users (receiver) movements in changing environments. QuaDRiGa supports Massive MIMO modelling enabled through a new multi-bounce scattering approach and spherical wave propagation. It will be continuously extended with features required by 5G and frequencies beyond 6 GHz. The QuaDRiGa model is supported by data from extensive channel measurement campaigns at 10 / 28 / 43 / 60 / 82 GHz performed by the same group.
ATIS Next G Alliance
- Published a report that includes a collection of results from several measurement campaigns relevant to deployment scenarios and use cases for 7-24 GHz channel modeling enhancements and channel modeling for integrated sensing and communications.
ETSI Industry specification group (ISG) Integrated sensing and communications (ISAC)
- ETSI ISG ISAC plans to publish Group Report (GR) /ISC-002 that provides information on advanced ISAC channel modeling concepts and their validation through measurement, in addition to information on evaluation methodology framework, deployment scenarios and the corresponding potentially suitable frequency bands.
6.2 Scenarios of interest #
Brief description of the key scenarios of interest identified (see note):
(1) UMi (Street canyon, open area) with O2O and O2I: This is similar to 3D-UMi scenario, where the BSs are mounted below rooftop levels of surrounding buildings. UMi open area is intended to capture real-life scenarios such as a city or station square. The width of the typical open area is in the order of 50 to 100 m.
Example: [Tx height:10 m, Rx height with reference to floor height: 1.5 m, ISD: 200 m]
(2) UMa with O2O and O2I: This is similar to 3D-UMa scenario, where the BSs are mounted above rooftop levels of surrounding buildings.
Example: [Tx height:25 m, Rx height with reference to floor height: 1.5 m, ISD: 200 m, 500 m]
(3) Indoor: This scenario is intended to capture various typical indoor deployment scenarios, including office environments, and shopping malls. The typical office environment is comprised of open cubicle areas, walled offices, open areas, corridors etc. The BSs are mounted at a height of 2-3 m either on the ceilings or walls. The shopping malls are often 1-5 stories high and may include an open area (or "atrium") shared by several floors. The BSs are mounted at a height of approximately 3 m on the walls or ceilings of the corridors and shops.
Example: [Tx height: 2-3 m, Rx height: 1.5 m, area: 500 square meters]
(4) Backhaul, including outdoor above roof top backhaul in urban area and street canyon scenario where small cell BSs are placed at lamp posts.
(5) D2D/V2V. Device-to-device access in open area, street canyon, and indoor scenarios. V2V is a special case where the devices are mobile.
(6) Other scenarios such as Stadium (open-roof) and Gym (close-roof).
(7) Indoor industrial scenarios
(8) Suburban macro (SMa) scenarios: In suburban macro-cells base stations are located above the surrounding environment to allow wide area coverage, and mobile stations are outdoors at street level and within commercial and residential buildings.
Example: [Tx height: 35 m, Rx height with reference to floor height: 1.5 m, ISD: 1299 m, 1732 m]
(9) Integrated sensing and communication scenarios as described in Clause 7.9.1.
Note: The scenarios of interest are based on the plenary email discussion and different from the supported scenarios in clause 7. The indoor industrial scenarios were identified at a later stage in the TSG RAN #81 meeting. SMa scenarios were identified at a later stage as part of Study on channel modelling enhancements for 7-24GHz for NR. The integrated sensing and communication scenarios were identified at a later stage as part of Study on channel modelling for integrated sensing and communications for NR.
6.3 Channel measurement capabilities #
The measurement capability as reported by each company is summarized in the following table.
Table 6.3-1: Channel measurement capabilities
| 6 - 20 GHz | 20 - 30 GHz | 30 - 60 GHz | >60 GHz |
Urban macro | CMCC Nokia/Aalborg Ericsson Vodafone BT Apple BUPT AT&T NTT Docomo Huawei | Nokia/Aalborg Ericsson
| NYU Ericsson
|
|
Urban micro | Aalto University CMCC Ericsson Intel/Fraunhofer HHI Nokia/Aalborg NTT DOCOMO Orange NYU/Sharp Apple BUPT AT&T Samsung Keysight Huawei Southeast University/ Purple Mountain Laboratories/China Telecom | AT&T Aalto University CMCC Huawei Intel/Fraunhofer HHI Nokia/Aalborg NTT DOCOMO NYU Qualcomm Samsung CATT KT ETRI ITRI/CCU ZTE Ericsson | AT&T Huawei Intel/Fraunhofer HHI NTT DOCOMO Qualcomm CATT ETRI ITRI/CCU ZTE Ericsson | AT&T Aalto University Huawei Intel/Fraunhofer HHI NYU |
Suburban macro | AT&T Ericsson Vodafone Apple Huawei | Ericsson Nokia | Ericsson |
|
Indoor | Aalto University CMCC Ericsson Huawei Intel/Fraunhofer HHI Nokia/Aalborg NTT DOCOMO Orange NYU/Sharp Apple Sony ZTE BUPT AT&T | AT&T Alcatel-Lucent Aalto University BUPT CMCC Huawei Intel/Fraunhofer HHI Nokia/Aalborg NTT DOCOMO NYU Qualcomm Samsung CATT KT ETRI ITRI/CCU ZTE | AT&T Ericsson Huawei Intel/Fraunhofer HHI NTT DOCOMO NYU Qualcomm CATT ETRI ITRI/CCU ZTE | AT&T Aalto University Huawei Intel/Fraunhofer HHI NYU |
O2I | Ericsson Huawei Intel/Fraunhofer HHI Nokia/Aalborg NTT DOCOMO Orange BT Vodafone | AT&T Alcatel-Lucent Ericsson Huawei Intel/Fraunhofer HHI NTT DOCOMO NYU Samsung KT Nokia | AT&T Ericsson Huawei Intel/Fraunhofer HHI NTT DOCOMO | AT&T Huawei Intel/Fraunhofer HHI |
6.4 Modelling objectives #
The requirements for channel modelling are as follows.
- Channel model SI should take into account the outcome of RAN-level discussion in the '5G' requirement study item.
- Complexity in terms of Description, Generating channel coefficients, development complexity and Simulation time should be considered.
- Support frequency range up to 100 GHz.
- The critical path of the SI is 6 – 100 GHz.
- Take care of mmW propagation aspects such as blocking and atmosphere attenuation.
- The model should be consistent in space, time and frequency.
- Support large channel bandwidths (up to 10% of carrier frequency).
- Aim for the channel model to cover a range of coupling loss considering current typical cell sizes, e.g. up to km-range macro cells. Note: This is to enable investigation of the relevance of the 5G system using higher frequency bands to existing deployments.
- Accommodate UT mobility
- Mobile speed up to 500 km/h.
- Develop a methodology considering that model extensions to D2D and V2V may be developed in future SI.
- Support large antenna arrays.
- Support near-field channel propagation (i.e., characteristics of spherical wavefront) and spatial non-stationarity (i.e. antenna element-wise power variation).
- Support integrated sensing and communications
7 Channel model(s) for 0.5-100 GHz #
7.1 Coordinate system #
7.1.1 Definition #
A coordinate system is defined by the x, y, z axes, the spherical angles and the spherical unit vectors as shown in Figure 7.1.1. Figure 7.1.1 defines the zenith angle 
and the azimuth angle
in a Cartesian coordinate system. Note that 
points to the zenith and 
points to the horizon. The field component in the direction of 
is given by 
and the field component in the direction of 
is given by 
.
Figure 7.1.1: Definition of spherical angles and spherical unit vectors in a Cartesian coordinate system, where
is the given direction, and are the spherical basis vectors
7.1.2 Local and global coordinate systems #
A Global Coordinate System (GCS) is defined for a system comprising multiple BSs and UTs. An array antenna for a BS or a UT can be defined in a Local Coordinate System (LCS). An LCS is used as a reference to define the vector far-field that is pattern and polarization, of each antenna element in an array. It is assumed that the far-field is known in the LCS by formulae. The placement of an array within the GCS is defined by the translation between the GCS and a LCS. The orientation of the array with respect to the GCS is defined in general by a sequence of rotations (described in clause 7.1.3). Since this orientation is in general different from the GCS orientation, it is necessary to map the vector fields of the array elements from the LCS to the GCS. This mapping depends only on the orientation of the array and is given by the equations in clause 7.1.3. Note that any arbitrary mechanical orientation of the array can be achieved by rotating the LCS with respect to the GCS.
7.1.3 Transformation from a LCS to a GCS #
A GCS with coordinates (x, y, z, , ) and unit vectors (, ) and an LCS with "primed" coordinates (x', y', z', 
, 
) and "primed" unit vectors (
, 
) are defined with a common origins in Figures 7.1.3-1 and 7.1.3-2. Figure 7.1.3-1 illustrates the sequence of rotations that relate the GCS (gray) and the LCS (blue). Figure 7.1.3-2 shows the coordinate direction and unit vectors of the GCS (gray) and the LCS (blue). Note that the vector fields of the array antenna elements are defined in the LCS. In Figure 7.1.3-1 we consider an arbitrary 3D-rotation of the LCS with respect to the GCS given by the angles α, β, γ. The set of angles α, β, γ can also be termed as the orientation of the array antenna with respect to the GCS.
Note that the transformation from a LCS to a GCS depends only on the angles α, β, γ. The angle α is called the bearing angle, β is called the downtilt angle and γ is called the slant angle.
|
|
Figure 7.1.3-1: Orienting the LCS (blue) with respect to the GCS (gray) by a sequence of 3 rotations: α, β, γ. | Figure 7.1.3-2: Definition of spherical coordinates and unit vectors in both the GCS and LCS. |
Let 
denote an antenna element pattern in the LCS and 
denote the same antenna element pattern in the GCS. Then the two are related simply by
(7.1-1)
with 
and 
given by (7.1-7) and (7.1-8).
Let us denote the polarized field components in the LCS by 
, 
and in the GCS by 
, 
. Then they are related by equation (7.1-11).
Any arbitrary 3D rotation can be specified by at most 3 elemental rotations, and following the framework of Figure 7.1.3-1, a series of rotations about the z, 
and 
axes are assumed here, in that order. The dotted and double-dotted marks indicate that the rotations are intrinsic, which means that they are the result of one () or two () intermediate rotations. In other words, the 
axis is the original y axis after the first rotation about z, and the axis is the original x axis after the first rotation about z and the second rotation about. A first rotation of α about z sets the antenna bearing angle (i.e. the sector pointing direction for a BS antenna element). The second rotation of β about sets the antenna downtilt angle. Finally, the third rotation of γ about sets the antenna slant angle. The orientation of the x, y and z axes after all three rotations can be denoted as 
, 
and 
. These triple-dotted axes represents the final orientation of the LCS, and for notational purposes denoted as the x', y' and z' axes (local or "primed" coordinate system).
In order to establish the equations for transformation of the coordinate system and the polarized antenna field patterns between the GCS and the LCS, it is necessary to determine the composite rotation matrix that describes the transformation of point (x, y, z) in the GCS into point (x', y', z') in the LCS. This rotation matrix is computed as the product of three elemental rotation matrices. The matrix to describe rotations about the z,and 
axes by the angles α, β and γ respectively and in that order is defined as
(7.1-2)
The reverse transformation is given by the inverse of R, which is also equal to the transpose of R since it is orthogonal.
(7.1-3)
The simplified forward and reverse composite rotation matrices are given by
(7.1-4)
and
(7.1-5)
These transformations can be used to derive the angular and polarization relationships between the two coordinate systems.
In order to establish the angular relationships, consider a point (x, y, z) on the unit sphere defined by the spherical coordinates (ρ=1, θ, φ), where ρ is the unit radius, θ is the zenith angle measured from the +z-axis, and φ is the azimuth angle measured from the +x-axis in the x-y plane. The Cartesian representation of that point is given by
(7.1-6)
The zenith angle is computed as 
and the azimuth angle as 
, where 
, 
and 
are the Cartesian unit vectors. If this point represents a location in the GCS defined by θ and φ, the corresponding position in the LCS is given by 
, from which local angles θ' and φ' can be computed. The results are given in equations (7.1-7) and (7.1-8).
(7.1-7)
(7.1-8)
These formulae relate the spherical angles (θ, φ) of the GCS to the spherical angles (θ', φ') of the LCS given the rotation operation defined by the angles (α, β, γ).
Let us denote the polarized field components, in the GCS and
, 
in the LCS. These are related by
(7.1-9)
In this equation, 
and 
represent the spherical unit vectors of the GCS, and 
and 
are the representations in the LCS. The forward rotation matrix R transforms the LCS unit vectors into the GCS frame of reference. These pairs of unit vectors are orthogonal and can be represented as shown in Figure 7.1.3-3.
Figure 7.1.3-3: Rotation of the spherical basis vectors by an angle ψ due to the orientation of the LCS with respect to the GCS
Assuming an angular displacement of ψ between the two pairs of unit vectors, the rotation matrix of equation (7.1-9) can be further simplified as:
(7.1-10)
and equation (7.1-9) can be written as:
(7.1-11)
The angle ψ can be computed in numerous ways from equation (7.1-10), with one such way approach being
(7.1-12)
The dot products are readily computed using the Cartesian representation of the spherical unit vectors. The general expressions for these unit vectors are given by
(7.1-13)
and
(7.1-14)
The angle ψ can be expressed as a function of mechanical orientation (α, β, γ) and spherical position (θ, φ), and is given by
(7.1-15)
It can be shown that
and
can be expressed as:
(7.1-16)
(7.1-17)
7.1.4 Transformation from an LCS to a GCS for downtilt angle only #
In this clause equations are provided for the transformation from LCS to GCS assuming that the orientation of the LCS (with respect to the GCS) is such that the bearing angle α=0, the downtilt angle β is non-zero and the slant angle γ=0. In other words the y'-axis of the LCS is parallel to the y-axis of the GCS. Considering a BS antenna element the x-axis of the GCS is aligned with the pointing direction of the sector. Mechanical downtilt is modelled as a rotation of the LCS around the y-axis. For zero mechanical downtilt the LCS coincides with the GCS.
This transformation relates the spherical angles (
,
) in the global coordinate system to spherical angles (
,
) in the local (antenna-fixed) coordinate system and is defined as follows:
(7.1-18)
(7.1-19)
where 
is the mechanical tilt angle around the y-axis as defined in Figure 7.1.4. Note that the equations (7.1-7), (7.1-8) reduce to equations (7.1-18), (7.1-19) if both α and γ are zero.
The antenna element pattern in the GCS is related to the antenna element pattern in the LCS by the relation
(7.1-20)
with and given by (7.1-18) and (7.1-19).
Figure 7.1.4: Definition of angles and unit vectors when the LCS has been rotated an angle 
around the y-axis of the GCS
For a mechanical tilt angle 
, the global coordinate system field components 
and 
, are calculated from the field components 
and 
of the radiation pattern in the local (antenna-fixed) coordinate system as:
(7.1-21)
(7.1-22)
where 
and 
are defined as in (7.1-18) and (7.1-19), and 
is defined as:
. (7.1-23)
Note that the equation (7.1-15) is reduced to equation (7.1-23) if both α and γ are zero.
As an example, in the horizontal cut, i.e., for 
, equations (7.1-18), (7.1-19) and (7.1-23) become
(7.1-24)
(7.1-25)
(7.1-26)
7.2 Scenarios #
The detailed scenario description in this clause can be used for channel model calibration.
UMi-street canyon and UMa
Details on UMi-street canyon and UMa scenarios are listed in Table 7.2-1.
Table 7.2-1: Evaluation parameters for UMi-street canyon and UMa scenarios
Parameters | UMi - street canyon | UMa | |
Cell layout | Hexagonal grid, 19 micro sites, 3 sectors per site (ISD = 200 m) | Hexagonal grid, 19 macro sites, 3 sectors per site (ISD = 500 m, see note 1) | |
BS antenna height | 10 m | 25 m | |
UT location | Outdoor/indoor | Outdoor and indoor | Outdoor and indoor |
LOS/NLOS | LOS and NLOS | LOS and NLOS | |
Height | Same as 3D-UMi in TR36.873 | Same as 3D-UMa in TR36.873 | |
Indoor UT ratio | 80% | 80% | |
UT mobility (horizontal plane only) | 3 km/h | 3 km/h | |
Min. BS - UT distance (2D) | 10 m | 35 m | |
UT distribution (horizontal) | Uniform | Uniform | |
NOTE 1: UMa scenarios with ISDs between 200-500m can be used for evaluations. | |||
Indoor-office
Details on indoor-office scenarios are listed in Table 7.2-2 and presented in Figure 7.2-1. More details, if necessary, can be added to Figure 7.2-1.
Table 7.2-2: Evaluation parameters for indoor-office scenarios
Parameters | Indoor - office open office | Indoor - office mixed office | |
Layout | Room size (W x L x H) | 120 m x 50 m x 3 m | |
ISD | 20 m | ||
BS antenna height | 3 m (ceiling) | ||
UT location | LOS/NLOS | LOS and NLOS
| |
Height | 1 m | ||
UT mobility (horizontal plane only) | 3 km/h | ||
Min. BS - UT distance (2D) | 0 | ||
UT distribution (horizontal) | Uniform | ||
Note: The only difference between the open office and mixed office models in this TR is the line of sight probability.
Figure 7.2-1: Layout of indoor office scenarios.
RMa
The rural deployment scenario focuses on larger and continuous coverage. The key characteristics of this scenario are continuous wide area coverage supporting high speed vehicles. This scenario will be noise-limited and/or interference-limited, using macro TRPs. Details of RMa scenario is described in Table 7.2-3.
Table 7.2-3: Evaluation parameters for RMa
Parameters | RMa |
Carrier Frequency | Up to 7 GHz |
BS height | 35 m |
Layout | Hexagonal grid, 19 Macro sites, 3 sectors per site, ISD = 1732 m or 5000 m |
UT height | 1.5 m |
UT distribution | Uniform |
Indoor/Outdoor | 50% indoor and 50% in car |
LOS/NLOS | LOS and NLOS |
Min BS - UT distance(2D) | 35 m |
Indoor Factory (InF)
The indoor factory (InF) scenario focuses on factory halls of varying sizes and with varying levels of density of "clutter", e.g. machinery, assembly lines, storage shelves, etc. Details of the InF scenario are listed in Table 7.2-4.
Table 7.2-4: Evaluation parameters for InF
| InF | |||||
Parameters | InF-SL (sparse clutter, low BS) | InF-DL (dense clutter, low BS) | InF-SH (sparse clutter, high BS) | InF-DH (dense clutter, high BS) | InF-HH (high Tx, high Rx) | |
Layout | Room size | Rectangular: 20-160000 m2 | ||||
Ceiling height | 5-25 m | 5-15 m | 5-25 m | 5-15 m | 5-25 m | |
| Effective clutter height | < Ceiling height, 0-10 m | ||||
| External wall and ceiling type | Concrete or metal walls and ceiling with metal-coated windows | ||||
Clutter type | Big machineries composed of regular metallic surfaces. For example: several mixed production areas with open spaces and storage/commissioning areas | Small to medium metallic machinery and objects with irregular structure. For example: assembly and production lines surrounded by mixed small-sized machineries. | Big machineries composed of regular metallic surfaces. For example: several mixed production areas with open spaces and storage/commissioning areas | Small to medium metallic machinery and objects with irregular structure. For example: assembly and production lines surrounded by mixed small-sized machineries. | Any | |
Typical clutter size, | 10 m | 2 m | 10 m | 2 m | Any | |
Clutter density | Low clutter density (<40%) | High clutter density (≥40%) | Low clutter density (<40%) | High clutter density (≥40%) | Any | |
BS antenna height | Clutter-embedded, i.e. the BS antenna height is below the average clutter height | Above clutter | Above clutter | |||
UT location | LOS/NLOS | LOS and NLOS | 100% LOS | |||
Height | Clutter-embedded | Above clutter | ||||
SMa
In suburban macro-cells base stations are located above the surrounding environment to allow wide area coverage, and mobile stations are outdoors at street level and within commercial and residential buildings. Buildings are typically low residential detached houses with one or two floors, or blocks of apartments/condos or commercial buildings with a few floors. Occasional open areas such as parks or playgrounds between the houses make the environment rather open. Streets do not form urban-like regular strict grid structure. In suburban areas, vegetation is more prevalent than in urban areas with a high variability of foliage density across different suburban areas. Details on SMa scenarios are listed in Table 7.2-5.
Table 7.2-5: Evaluation parameters for SMa scenarios
Parameters | SMa | |
Cell layout | Hexagonal grid, 19 macro sites, 3 sectors per site (ISD = 1299 m and 1732 m, see note 1 and 2) Up to two floors for residential buildings, up to five floors for commercial buildings. Building distribution are 90% residential and 10% commercial buildings | |
BS antenna height hBS | 35 m | |
UT location | Outdoor/indoor | Outdoor and indoor |
LOS/NLOS | LOS and NLOS | |
Height hUT | 1.5 m for outdoor, 1.5 or 4.5 m for residential buildings, 1.5/4.5/7.5/10.5/13.5 m for commercial buildings | |
Indoor UT ratio | 80% | |
UT mobility (horizontal plane only) | Indoor UTs: 3 km/h Outdoor UTs (in car): 40 km/h | |
Min. BS - UT distance (2D) | 35 m | |
UT distribution (horizontal) | Uniform | |
UT distribution (vertical) | Uniform distribution across all floors for a building type | |
N OTE 1: For evaluation in study items/work items, if needed, admission control policies may be considered in conjunction with the ISD choice to address out of coverage UEs NOTE 2: SMa scenarios with ISDs between 1200-1800m can be used for evaluations. | ||
7.3 Antenna modelling #
7.3.0 Antenna array structure #
This clause captures the antenna array structures considered in this SI for calibration.
BS antenna model:
The BS antenna is modelled by a uniform rectangular panel array, comprising MgNg panels, as illustrated in Figure 7.3-1 with Mg being the number of panels in a column and Ng being the number of panels in a row. Furthermore the following properties apply:
- Antenna panels are uniformly spaced in the horizontal direction with a spacing of dg,H and in the vertical direction with a spacing of dg,V.
- On each antenna panel, antenna elements are placed in the vertical and horizontal direction, where N is the number of columns, M is the number of antenna elements with the same polarization in each column.
- Antenna numbering on the panel illustrated in Figure 7.3-1 assumes observation of the antenna array from the front (with x-axis pointing towards broad-side and increasing y-coordinate for increasing column number).
- The antenna elements are uniformly spaced in the horizontal direction with a spacing of dH and in the vertical direction with a spacing of dV.
- The antenna panel is either single polarized (P =1) or dual polarized (P =2).
The rectangular panel array antenna can be described by the following tuple 
.
Figure 7.3-1: Cross-polarized panel array antenna model
The antenna radiation power pattern of each antenna element is generated according to Table 7.3-1.
Table 7.3-1: Radiation power pattern of a single antenna element
Parameter | Values |
Vertical cut of the radiation power pattern (dB) |
|
Horizontal cut of the radiation power pattern (dB) |
|
3D radiation power pattern (dB) |
|
Maximum directional gain of an antenna element GE,max | 8 dBi |
UT antenna model:
The UT antenna can be modelled by placing the antenna element/array at the placement candidate locations relative to the centre of the UT. The size of the device can be set to (X cm, Y cm, Z cm), in depth, width, and height respectively.
For handheld UT devices, the following additional properties apply:
- Four corners of the rectangle and the center of edges of the rectangle that reflect UT form factor are identified as potential locations of the UT antenna and potential locations are enumerated as illustrated in Figure 7.3-2.
- Each antenna is assumed to be oriented along the direction determined by the vector connecting the centre of the rectangle to the antenna location.
- Reference UT orientation vector of the handheld UT is perpendicular to the plane of the flat UT handheld device, and reference point for near field phase calculation of the UT is assumed to be the center of the plane of the UT handheld.
- Device dimensions for UT antenna modelling are (15 cm, 7 cm, 0 cm).
Figure 7.3-2: Handheld UT antenna placement candidate locations relative to centre of device (top down view)
- Reference handheld UT orientation is illustrated in Figure 7.3-3. The representation the handheld UT orientation will be same between GCS and UT LCS for ΩUT,α = 0 degrees, ΩUT,β = 0 degrees, ΩUT,γ = 0 degrees,
Figure 7.3-3: Illustration of reference handheld UT orientation in UT LCS
- For calibration of handheld UT, the following UT rotation based on reference UT orientation is assumed.
- For calibration with spatial non-stationarity modelling:
- For one-hand blockage, ΩUT,α = 0 – 360 degrees, ΩUT,β = 45 degrees, ΩUT,γ = 0 degrees,
- For dual-hand blockage, ΩUT,α = 0 – 360 degrees, ΩUT,β = 0 degrees, ΩUT,γ = 45 degrees,
- For hand and head blockage, ΩUT,α = 0 – 360 degrees, ΩUT,β = 90 degrees, ΩUT,γ = 0 degrees,
- For all other cases:
- ΩUT,α = 0 – 360 degrees, ΩUT,β = 45 degrees, ΩUT,γ = 0 degrees,
- Example of UT rotations are illustrated in Figure 7.3-4 and 7.3-5.
Figure 7.3-4: Example of UT rotation of ΩUT,α = 0 - 360 degrees, ΩUT,β = 90 degrees, ΩUT,γ = 0 degrees
Figure 7.3-5: Example of UT rotation of ΩUT,α = 0 - 360 degrees, ΩUT,β = 0 degrees, ΩUT,γ = 90 degrees
- The antenna radiation power pattern of each antenna element for handheld UT is generated according to Table 7.3-2, which assumes antenna is oriented in
Table 7.3-2: Radiation power pattern of a single antenna element for handheld UT
Parameter | Values |
Vertical cut of the radiation power pattern (dB) | |
Horizontal cut of the radiation power pattern (dB) | |
3D radiation power pattern (dB) | |
Maximum directional gain of an antenna element GE,max | 5.3 dBi |
NOTE: For UT antenna modelling of handheld devices, optional antenna imbalance can be modelled. If modelled, randomized loss is applied per UT antenna port and randomized loss can be applied independently for the UL and DL directions. No imbalance is modelled by default. | |
For consumer premise equipment (CPE) devices, the following properties apply:
- Total 9 candidate antenna locations in 4 corners of the vertical square plane, 4 center of the edges of the vertical square plane, and center of the vertical square plane. The figure below captures the side view on the device. Note that a candidate antenna location (e.g., 9) can be used for multiple antennas.
- Device dimensions for UT antenna modelling are (0 cm, 20 cm, 20 cm)
Figure 7.3-6: CPE UT antenna placement candidate locations relative to centre of device (side view)
7.3.1 Antenna port mapping #
Legacy BS array antennas, i.e. uniform linear arrays with fix phase shifts between its M elements to obtain a beamtilt in vertical direction are modelled using complex weights
where m=1, …, M,
7.3.2 Polarized antenna modelling #
In general the relationship between radiation field and power pattern is given by:
The following two models represent two options on how to determine the radiation field patterns based on a defined radiation power pattern.
Model-1:
In case of polarized antenna elements assume
where
Note that the zenith and the azimuth field components
Model-2:
In case of polarized antennas, the polarization is modelled as angle-independent in both azimuth and elevation, in an LCS. For a linearly polarized antenna, the antenna element field pattern, in the vertical polarization and in the horizontal polarization, are given by
and
respectively, where
Handheld UT Model:
For cases when a candidate antenna placement location is used for one antenna field pattern:
- Reference radiation pattern of the UT antenna model is vertically polarized with all the gain in the theta field component,
For cases when a candidate antenna placement location is used for two distinct antenna polarization field patterns:
- Reference radiation pattern of the UT antenna model is,
- For first antenna field pattern:
- For second antenna field pattern:
Each polarized field component of the reference radiation pattern
where the 3D-rotation angles,
For cases when a candidate antenna placement location is used for one antenna field pattern (e.g., single polarization):
- The polarization direction is indicated by the arrow in Figure 7.3-7, which is parallel with the plane of the handheld UT and perpendicular to the direction from the UT center to the candidate antenna location.
Figure 7.3-7: Handheld UT antenna polarization directions for one antenna field pattern (top down view)
For cases when a candidate antenna placement location is used for two antenna field patterns (e.g., dual polarization) (not intended for FR1):
- For the first antenna field pattern, the polarization direction is indicated by the arrows in Figure 7.3-7 but additionally rotated 45 degrees about the direction from the UT center to the candidate antenna location (i.e. rotated using the direction from the UT center to the candidate antenna location as the rotational axis).
- For the second antenna field pattern, the polarization direction is perpendicular to the polarization direction of the first filed pattern and perpendicular to the direction from the UT center to the candidate antenna location
- An example for candidate antenna location (6) is given in Figure 7.3-8.
Figure 7.3-8: Handheld UT polarization direction for two antenna filed pattern (side view)
7.4 Pathloss, LOS probability and penetration modelling #
7.4 .1 Pathloss #
The pathloss models are summarized in Table 7.4.1-1 and the distance definitions are indicated in Figure 7.4.1-1 and Figure 7.4.1-2. Note that the distribution of the shadow fading is log-normal, and its standard deviation for each scenario is given in Table 7.4.1-1.
|
|
Figure7.4.1-1: Definition of d2D and d3D for outdoor UTs<br> | Figure 7.4.1-2: Definition of d2D-out, d2D-in and <br>d3D-out, d3D-in for indoor UTs. |
Note that
(7.4-1)
Table 7.4.1-1: Pathloss models
Scenario | LOS/NLOS | Pathloss [dB], fc is in GHz and d is in meters, see note 6 | Shadow fading std [dB] | Applicability range, antenna height default values |
RMa | LOS |
|
For 10 m ≤ d2D <
For
| h = avg. building height W = avg. street width The applicability ranges: |
NLOS |
|
| ||
UMa | LOS |
|
| See note 7 |
NLOS |
|
| Explanations: see note 3 | |
Optional |
| |||
UMi - Street Canyon | LOS |
| See note 7 | |
NLOS |
| Explanations: see note 4 | ||
Optional |
| |||
InH - Office | LOS | |||
NLOS | ||||
Optional | ||||
InF | LOS | |||
NLOS | InF-SL: | |||
InF-DL: | ||||
InF-SH: | ||||
InF-DH: | ||||
SMa | LOS |
| For 10 m ≤ d2D < For | 10 m ≤ d2D < dBP dBP ≤ d2D < 5000 m 25 m < hBS < 35 m h = 10 m |
NLOS | 10 m ≤ d2D < 5000 m 1 m < hUT < 14 m h = 10 m, W = 10 m | |||
NOTE 1: Effective breakpoint distance d'BP = 4 h'BS h'UT fc/c, where fc is the centre frequency in Hz, c = 3.0108 m/s is the propagation velocity in free space, and h'BS and h'UT are the effective antenna heights at the BS and the UT, respectively. The effective antenna heights h'BS and h'UT are computed as follows: h'BS = hBS – hE, h'UT = hUT – hE, where hBS and hUT are the actual antenna heights, and hE is the effective environment height. For UMi hE = 1.0m. For UMa hE=1m with a probability equal to 1/(1+C(d2D, hUT)) and chosen from a discrete uniform distribution uniform(12,15,…,(hUT-1.5)) otherwise. With C(d2D, hUT) given by where Note that hE depends on d2D and hUT and thus needs to be independently determined for every link between BS sites and UTs. A BS site may be a single BS or multiple co-located BSs. NOTE 2: The applicable frequency range of the PL formula in this table is 0.5 < fc < fH GHz, where fH = 30 GHz for RMa, fH = 37 GHz for SMa, and fH = 100 GHz for all the other scenarios. It is noted that RMa pathloss model for >7 GHz is validated based on a single measurement campaign conducted at 24 GHz. NOTE 3: UMa NLOS pathloss is from TR36.873 with simplified format and PLUMa-LOS = Pathloss of UMa LOS outdoor scenario. NOTE 4: PLUMi-LOS = Pathloss of UMi-Street Canyon LOS outdoor scenario. NOTE 5: Break point distance dBP = 2π hBS hUT fc/c, where fc is the centre frequency in Hz, c = 3.0 108 m/s is the propagation velocity in free space, and hBS and hUT are the antenna heights at the BS and the UT, respectively. NOTE 6: fc denotes the center frequency normalized by 1GHz, all distance related values are normalized by 1m, unless it is stated otherwise. NOTE 7: UMi and UMa pathloss formula converges for different frequencies when distances beyond the breakpoint distance is applied. | ||||
7.4 .2 LOS probability #
The Line-Of-Sight (LOS) probabilities are given in Table 7.4.2-1.
Table 7.4.2-1 LOS probability
Scenario | LOS probability (distance is in meters) |
RMa |
|
UMi - Street canyon |
|
UMa |
|
Indoor - Mixed office |
|
Indoor - Open office |
|
InF-SL InF-SH InF-DL InF-DH | where The parameters |
InF-HH | |
SMa | where |
NOTE: The LOS probability is derived with assuming antenna heights of 3m for indoor, 10m for UMi, and 25m for UMa | |
where
7.4 .3 O2I penetration loss #
7.4 .3.1 O2I building penetration loss #
The pathloss incorporating O2I building penetration loss is modelled as in the following:
where
Table 7.4.3-1: Material penetration losses
Material | Penetration loss [dB] | Notes on Material |
Standard multi-pane glass | Reference thickness of 3 cm assumed | |
IRR glass | Single coating is assumed | |
Concrete | Reference thickness of 23 cm assumed | |
Plywood | Reference thickness of 1.75 cm assumed | |
Wood | Reference thickness of 3.3 cm assumed | |
NOTE 1: f is in GHz NOTE 2: Values marked with this note were updated as part of Release 19 Study on channel modelling enhancements for 7-24 GHz for NR. | ||
Table 7.4.3-2 gives
Table 7.4.3-2: O2I building penetration loss model
| Path loss through external wall: | Indoor loss: | Standard deviation: σP in [dB] |
Low-loss model | 0.5 | 4.4 | |
High-loss model | 0.5 | 6.5 | |
Low-loss A model | 0.5 | 4.4 |
Both low-loss and high-loss models are applicable to UMa and UMi-Street Canyon.
Only the low-loss model is applicable to RMa.
Only the high-loss model is applicable to InF.
Low-loss, high-loss, and low-loss A models are applicable to SMa.
The composition of low and high loss is a simulation parameter that should be determined by the user of the channel models, and is dependent on the use of metal-coated glass in buildings and the deployment scenarios. Such use is expected to differ in different markets and regions of the world and also may increase over years to new regulations and energy saving initiatives. Furthermore, the use of such high-loss glass currently appears to be more predominant in commercial buildings than in residential buildings in some regions of the world (see note).
Note: One example survey for the US market can be found in [5]. The survey does not necessarily be representative for all the scenarios. Other ratios outside of the survey should not be precluded.
For backwards compatibility with TR 36.873 [3], the following building penetration model should be used for UMa and UMi single-frequency simulations at frequencies below 6 GHz.
Table 7.4.3-3. O2I building penetration loss model for single-frequency simulations <6 GHz
Parameter | Value |
| 20 dB |
| 0.5 with |
0 dB | |
7 dB (note: replacing the respective value in Table 7.4.1-1) |
7.4 .3.2 O2I car penetration loss #
The pathloss incorporating O2I car penetration loss is modelled as in the following:
(7.4-4)
where 
is the basic outdoor path loss given in Clause 7.4.1. μ = 9, and σP = 5. The car penetration loss shall be UT-specifically generated. Optionally, for metallized car windows, μ = 20 can be used. The O2I car penetration loss models are applicable for at least 0.6-60 GHz.
7.4.4 Autocorrelation of shadow fading #
The long-term (log-normal) fading in the logarithmic scale around the mean path loss PL (dB) is characterized by a Gaussian distribution with zero mean and standard deviation. Due to the slow fading process versus distance x (<br>x is in the horizontal plane), adjacent fading values are correlated. Its normalized autocorrelation function R(x) can be described with sufficient accuracy by the exponential function ITU-R Rec. P.1816 [18]
(7.4-5)
with the correlation length dcor being dependent on the environment, see the correlation parameters for shadowing and other large scale parameters in Table 7.5-6 (Channel model parameters). In a spatial consistency procedure in Clause 7.6.3, the cluster specific random variables are also correlated following the exponential function with respect to correlation distances in the two dimensional horizontal plane.
7.5 Fast fading model #
The radio channel realizations are created using the parameters listed in Table 7.5-1. The channel realizations are obtained by a step-wise procedure illustrated in Figure 7.5-1 and described below. It has to be noted that the geometric description covers arrival angles from the last bounce scatterers and respectively departure angles to the first scatterers interacted from the transmitting side. The propagation between the first and the last interaction is not defined. Thus, this approach can model also multiple interactions with the scattering media. This indicates also that e.g., the delay of a multipath component cannot be determined by the geometry. In the following steps, downlink is assumed. For uplink, arrival and departure parameters have to be swapped.
Note: the channel generation in this clause is enough for at least the following cases.
- Case 1: For low complexity evaluations
- Case 2: To compare with earlier simulation results,
- Case 3: When none of the additional modelling components are turned on.
For other advanced simulations, e.g., spatially consistency, large bandwidth and arrays, oxygen absorption, blockage, absolute time of arrival, dual mobility, embedded devices, etc., some of the additional modelling components of Clause 7.6 should be considered.
Figure 7.5-1 Channel coefficient generation procedure
Figure 7.5-2: Definition of a global coordinate system showing the zenith angle θ and the azimuth angle ϕ. θ=0° points to zenith and θ=+90° points to the horizon. The spherical basis vectors <br> and shown above are defined based on the direction of propagation.
Table 7.5-1: Notations in the global coordinate system (GCS)
Parameter | Notation |
LOS AOD | ϕLOS,AOD |
LOS AOA | ϕLOS,AOA |
LOS ZOD | θLOS,ZOD |
LOS ZOA | θLOS,ZOA |
AOA for cluster n | ϕn,AOA |
AOD for cluster n | ϕn,AOD |
AOA for ray m in cluster n | ϕn,m,AOA |
AOD for ray m in cluster n | ϕn,m,AOD |
ZOA for cluster n | θn,ZOA |
ZOD for cluster n | θn,ZOD |
ZOA for ray m in cluster n | θn,m,ZOA |
ZOD for ray m in cluster n | θn,m,ZOD |
Receive antenna element u field pattern in the direction of the spherical basis vector | Frx,u,θ |
Receive antenna element u field pattern in the direction of the spherical basis vector | Frx,u,ϕ |
Transmit antenna element s field pattern in the direction of the spherical basis vector | Ftx,s,θ |
Transmit antenna element s field pattern in the direction of the spherical basis vector | Frx,s,ϕ |
Step 1: Set environment, network layout, and antenna array parameters
a) Choose one of the scenarios (e.g. UMa, UMi-Street Canyon, RMa, InH-Office or InF). Choose a global coordinate system and define zenith angle θ, azimuth angle ϕ, and spherical basis vectors , as shown in Figure 7.5-2. Note: Scenario RMa is for up to 7GHz while others are for up to 100GHz
b) Give number of BS and UT
c) Give 3D locations of BS and UT, and determine LOS AOD (ϕLOS,AOD), LOS ZOD (θLOS,ZOD), LOS AOA (ϕLOS,AOA), and LOS ZOA (θLOS,ZOA) of each BS and UT in the global coordinate system
d) Give BS and UT antenna field patterns Frx and Ftx in the global coordinate system and array geometries
e) Give BS and UT orientations with respect to the global coordinate system. BS array orientation is defined by three angles ΩBS,α (BS bearing angle), ΩBS,β (BS downtilt angle) and ΩBS,γ (BS slant angle). UT orientation is defined by three angles ΩUT,α (UT bearing angle), ΩUT,β (UT downtilt angle) and ΩUT,γ (UT slant angle).
f) Give speed and direction of motion of UT in the global coordinate system
g) Specify system centre frequency 
and bandwidth 
Note: In case wrapping is used, each wrapping copy of a BS or site should be treated as a separate BS/site considering channel generation.
Large scale parameters:
Step 2: Assign propagation condition (LOS/NLOS) according to Table 7.4.2-1. The propagation conditions for different BS-UT links are uncorrelated.
Also, assign an indoor/outdoor state for each UT. It is noted that all the links from a UT have the same indoor/outdoor state.
Step 3: Calculate pathloss with formulas in Table 7.4.1-1 for each BS-UT link to be modelled.
Step 4: Generate large scale parameters, e.g. delay spread (DS), angular spreads (ASA, ASD, ZSA, ZSD), Ricean K factor (K) and shadow fading (SF) taking into account cross correlation according to Table 7.5-6 and using the procedure described in clause 3.3.1 of [14] with the square root matrix
being generated using the Cholesky decomposition and the following order of the large scale parameter vector: sM = [sSF, sK, sDS, sASD, sASA, sZSD, sZSA]T.
These LSPs for different BS-UT links are uncorrelated, but the LSPs for links from co-sited sectors to a UT are the same. In addition, these LSPs for the links of UTs on different floors are uncorrelated.
Limit random RMS azimuth arrival and azimuth departure spread values to 104 degrees, i.e., ASA= min(ASA, 104), ASD = min(ASD, 104). Limit random RMS zenith arrival and zenith departure spread values to 52 degrees, i.e., ZSA = min(ZSA, 52), ZSD = min(ZSD, 52).
Small scale parameters:
Step 5: Generate cluster delays 
Delays are drawn randomly from the delay distribution defined in Table 7.5-6. With exponential delay distribution calculate
, (7.5-1)
Where rτ is the delay distribution proportionality factor, Xn ~ uniform(0,1), and cluster index n = 1,…,N. With uniform delay distribution the delay values 
are drawn from the corresponding range. Normalise the delays by subtracting the minimum delay and sort the normalised delays to ascending order:
. (7.5-2)
In the case of LOS condition, additional scaling of delays is required to compensate for the effect of LOS peak addition to the delay spread. The heuristically determined Ricean K-factor dependent scaling constant is
, (7.5-3)
where K [dB] is the Ricean K-factor as generated in Step 4. The scaled delays
, (7.5-4)
are not to be used in cluster power generation.
Step 6: Generate cluster powers 
.
Cluster powers are calculated assuming a single slope exponential power delay profile. Power assignment depends on the delay distribution defined in Table 7.5-6. With exponential delay distribution the cluster powers are determined by
(7.5-5)
where 
is the per cluster shadowing term in [dB]. Normalize the cluster powers so that the sum of all cluster powers is equal to one, i.e.,
(7.5-6)
In the case of LOS condition an additional specular component is added to the first cluster. Power of the single LOS ray is:
(7.5-7)
and the cluster powers are not normalized as in equation (7.5-6) , but:
(7.5-8)
where δ(.) is Dirac's delta function and KR is the Ricean K-factor as generated in Step 4 converted to linear scale. These power values are used only in equations (7.5-9) and (7.5-14), but not in equation (7.5-22).
Assign the power of each ray within a cluster as Pn / M, where M is the number of rays per cluster.
Remove clusters with less than -25 dB power compared to the maximum cluster power based on equation (7.5-6). The scaling factors need not be changed after cluster elimination.
Step 7: Generate arrival angles and departure angles for both azimuth and elevation.
The composite PAS in azimuth of all clusters is modelled as wrapped Gaussian. The AOAs are determined by applying the inverse Gaussian function (7.5-9) with input parameters Pn and RMS angle spread ASA
, (7.5-9)
with 
defined as
, (7.5-10)
where 
is defined as a scaling factor related to the total number of clusters and is given in Table 7.5-2:
Table 7.5-2: Scaling factors for AOA, AOD generation
# clusters | 4 | 5 | 6 | 7 | 8 |
| 10 | 11 | 12 |
0.779 | 0.860 | 0.921 | 0.973 | 1.018 |
| 1.090 | 1.123 | 1.146 | |
# clusters |
| 14 | 15 | 16 |
|
| 19 | 20 | 25 |
| 1.190 | 1.211 | 1.226 |
|
| 1.273 | 1.289 | 1.358 |
In the LOS case, constant also depends on the Ricean K-factor K in [dB], as generated in Step 4. Additional scaling of the angles is required to compensate for the effect of LOS peak addition to the angle spread.
Assign positive or negative sign to the angles by multiplying with a random variable Xn with uniform distribution to the discrete set of {1,–1}, and add component 
to introduce random variation
, (7.5-11)
where ϕLOS,AOA is the LOS direction defined in the network layout description, see Step1c.
In the LOS case, substitute (7.5-11) by (7.5-12) to enforce the first cluster to the LOS direction ϕLOS, AOA
(7.5-12)
Finally add offset angles αm from Table 7.5-3 to the cluster angles
, (7.5-13)
where cASA is the cluster-wise rms azimuth spread of arrival angles (cluster ASA) in Table 7.5-6.
Table 7.5-3: Ray offset angles within a cluster, given for rms angle spread normalized to 1
Ray number m | Basis vector of offset angles αm |
1,2 | ± 0.0447 |
3,4 | ± 0.1413 |
5,6 | ± 0.2492 |
7,8 | ± 0.3715 |
9,10 | ± 0.5129 |
11,12 | ± 0.6797 |
13,14 | ± 0.8844 |
15,16 | ± 1.1481 |
17,18 | ± 1.5195 |
19,20 | ± 2.1551 |
The generation of AOD (
) follows a procedure similar to AOA as described above.
The generation of ZOA assumes that the composite PAS in the zenith dimension of all clusters is Laplacian (see Table 7.5-6). The ZOAs are determined by applying the inverse Laplacian function (7.5-14) with input parameters Pn and RMS angle spread ZSA
, (7.5-14)
with 
defined as
, (7.5-15)
Where 
is a scaling factor related to the total number of clusters and is given in Table 7.5-4:
Table 7.5-4: Scaling factors for ZOA, ZOD generation
# clusters |
|
| 6 | 7 | 8 |
| 10 | 11 | 12 |
|
| 0.788 | 0.847 | 0.889 |
| 0.957 | 1.031 | 1.104 | |
# clusters |
| 14 | 15 | 16 |
|
| 19 | 20 | 25 |
| 1.1072 | 1.1088 | 1.1276 |
|
| 1.184 | 1.178 | 1.282 |
In the LOS case, constant 
also depends on the Ricean K-factor K in [dB], as generated in Step 4. Additional scaling of the angles is required to compensate for the effect of LOS peak addition to the angle spread.
Assign positive or negative sign to the angles by multiplying with a random variable Xn with uniform distribution to the discrete set of {1,–1}, and add component 
to introduce random variation
, (7.5-16)
where 
if the BS-UT link is O2I and 
otherwise. The LOS direction is defined in the network layout description, see Step1c.
In the LOS case, substitute (7.5-16) by (7.5-17) to enforce the first cluster to the LOS direction θLOS,ZOA
. (7.5-17)
Finally add offset angles αm from Table 7.5-3 to the cluster angles
, (7.5-18)
where cZSA is the cluster-wise rms spread of ZOA (cluster ZSA) in Table 7.5-6. Assuming that 
is wrapped within [0, 360°], if 
, then
is set to 
.
The generation of ZOD follows the same procedure as ZOA described above except equation (7.5-16) is replaced by
, (7.5-19)
where variable Xn is with uniform distribution to the discrete set of {1,–1}, 
, 
is given in Tables 7.5-6/7/8 and equation (7.5-18) is replaced by
(7.5-20)
where 
is the mean of the ZSD log-normal distribution.
In the LOS case, the generation of ZOD follows the same procedure as ZOA described above using equation (7.5-17).
Step 8: Coupling of rays within a cluster for both azimuth and elevation
Couple randomly AOD angles φn,m,AOD to AOA angles φn,m,AOA within a cluster n, or within a sub-cluster in the case of two strongest clusters (see Step 11 and Table 7.5-3). Couple randomly ZOD angles 
with ZOA angles 
using the same procedure. Couple randomly AOD angles φn,m,AOD with ZOD angles within a cluster n or within a sub-cluster in the case of two strongest clusters.
Step 9: Generate the cross polarization power ratios
Generate the cross polarization power ratios (XPR) κfor each ray m of each cluster n. XPR is log-Normal distributed. Draw XPR values as
, (7.5-21)
where 
is Gaussian distributed with 
and 
from Table 7.5-6.
Note: 
is independently drawn for each ray and each cluster.
The outcome of Steps 1-9 shall be identical for all the links from co-sited sectors to a UT.
Coefficient generation:
Step 10: Draw initial random phases
Draw random initial phase 
for each ray m of each cluster n and for four different polarisation combinations (θθ, θϕ, ϕθ, ϕϕ). The distribution for initial phases is uniform within (-ππ).
Step 11: Generate channel coefficients for each cluster n and each receiver and transmitter element pair u, s.
The method described below is used at least for drop-based evaluations irrespective of UT speeds. Relevant cases for drop-based evaluations are:
- Case 1: For low complexity evaluations
- Case 2: To compare with earlier simulation results,
- Case 3: When none of the additional modelling components are turned on.
- Case 4: When spatial consistency and/or blockage is modeled for MU-MIMO simulations
- Other cases are not precluded
For the N – 2 weakest clusters, say n = 3, 4,…, N, the channel coefficients are given by:
(7.5-22)
where Frx,u,θ and Frx,u,ϕ are the field patterns of receive antenna element u according to (7.1-11) and in the direction of the spherical basis vectors, and respectively, Ftx,s,θ and Ftx,s,ϕ are the field patterns of transmit antenna element s in the direction of the spherical basis vectors, and 
respectively. Note that the patterns are given in the GCS and therefore include transformations with respect to antenna orientation as described in Clause 7.1. is the spherical unit vector with azimuth arrival angle ϕn,m,AOA and elevation arrival angle θn,m,ZOA, given by
, (7.5-23)
where n denotes a cluster and m denotes a ray within cluster n. is the spherical unit vector with azimuth departure angle ϕn,m,AOD and elevation departure angle θn,m,ZOD, given by
, (7.5-24)
where n denotes a cluster and m denotes a ray within cluster n. Also, 
is the location vector of receive antenna element u and 
is the location vector of transmit antenna element s, κn,m is the cross polarisation power ratio in linear scale, and λ0 is the wavelength of the carrier frequency. If polarisation is not considered, the 2x2 polarisation matrix can be replaced by the scalar 
and only vertically polarised field patterns are applied.
The Doppler frequency component depends on the arrival angles (AOA, ZOA), and the UT velocity vector 
with speed v, travel azimuth angle ϕv, elevation angle θv and is given by
(7.5-25)
For the two strongest clusters, say n = 1 and 2, rays are spread in delay to three sub-clusters (per cluster), with fixed delay offset. The delays of the sub-clusters are
(7.5-26)
where 
is cluster delay spread specified in Table 7.5-6. When intra-cluster delay spread is unspecified (i.e., N/A) the value 3.91 ns is used; it is noted that this value results in the legacy behaviour with 5 and 10 ns sub-cluster delays.
Twenty rays of a cluster are mapped to sub-clusters as presented in Table 7.5-5 below. The corresponding offset angles are taken from Table 7.5-3 with mapping of Table 7.5-5.
Table 7.5-5: Sub-cluster information for intra cluster delay spread clusters
sub-cluster #
| mapping to rays
| Power
| delay offset
|
|
| 10/20 | 0 |
|
| 6/20 | 1.28 |
|
| 4/20 | 2.56 |
Then, the channel impulse response is given by:
(7.5-27)
where 
is given in (7.5-22) and 
defined as:
(7.5-28)
In the LOS case, determine the LOS channel coefficient by:
(7.5-29)
where δ(.) is the Dirac's delta function and KR is the Ricean K-factor as generated in Step 4 converted to linear scale.
Then, the channel impulse response is given by adding the LOS channel coefficient to the NLOS channel impulse response and scaling both terms according to the desired K-factor 
as
. (7.5-30)
Step 12: Apply pathloss and shadowing for the channel coefficients.
Table 7.5-6 Part-1: Channel model parameters for UMi-Street Canyon and UMa
Scenarios | UMi - Street Canyon | UMa | |||||
LOS | NLOS | O2I | LOS | NLOS | O2I | ||
Delay spread (DS) lgDS=log10(DS/1s) | μlgDS | -0.18 log10(1+ fc) - 7.28, see note 8 | -0.22 log10(1+ fc) - 6.87, see note 8 | -6.62 | -7.067 - 0.0794 log10(fc), see note 8 | -6.47 - 0.134 log10(fc), see note 8 | -6.62 |
σlgDS | 0.39, see note 8 | 0.19 log10(1+ fc) + 0.22, see note 8 | 0.32 | 0.57 + 0.026 log10(fc), see note 8 | 0.39 see note 9 | 0.32 | |
AOD spread (ASD) lgASD=log10(ASD/1) | μlgASD | -0.05 log10(1+ fc) + 1.21 see note 9 | -0.24 log10(1+ fc) + 1.54 see note 8 | 1.25 | 0.92, see note 8 | 1.09, see note 8 | 0.58, see note 8 |
σlgASD | 0.08 log10(1+ fc) + 0.29 see note 8 | 0.10 log10(1+ fc) + 0.33 see note 8 | 0.42 | 0.31, see note 8 | 0.44, see note 8 | 0.7 see note 8 | |
AOA spread (ASA) lgASA=log10(ASA/1) | μlgASA | -0.07 log10(1+ fc) + 1.66, see note 8 | -0.07 log10(1+ fc) + 1.76, see note 8 | 1.76 | 1.76, see note 8 | 2.04 - 0.25 log10(fc), see note 8 | 1.76 |
σlgASA | 0.021 log10(1+ fc) + 0.26, see note 8 | 0.05 log10(1+ fc) + 0.27, see note 8 | 0.16 | 0.19, see note 8 | 0.17 - 0.03 log10(fc), see note 8 | 0.16 | |
ZOA spread (ZSA) lgZSA=log10(ZSA/1) | μlgZSA | -0.11 log10(1+ fc) + 0.81 see note 8 | -0.03 log10(1+ fc) + 0.92 see note 8 | 1.01 | 0.96 see note 8 | -0.2856 log10(fc) + 1.445, see note 8 | 1.01 |
σlgZSA | -0.03 log10(1+ fc) + 0.29 see note 8 | -0.05 log10(1+ fc) + 0.35 see note 8 | 0.43 | 0.15 see note 8 | 0.17, see note 8 | 0.43 | |
Shadow fading (SF) [dB] | σSF | See Table 7.4.1-1 | See Table 7.4.1-1 | 7 | See Table 7.4.1-1 | See Table 7.4.1-1 | 7 |
K-factor (K) [dB] | μK | 9 | N/A | N/A | 9 | N/A | N/A |
σK | 5 | N/A | N/A | 3.5 | N/A | N/A | |
Cross-Correlations | ASD vs DS | 0.5 | 0 | 0.4 | 0.4 | 0.4 | 0.4 |
ASA vs DS | 0.8 | 0.4 | 0.4 | 0.8 | 0.6 | 0.4 | |
ASA vs SF | -0.4 | -0.4 | 0 | -0.5 | 0 | 0 | |
ASD vs SF | -0.5 | 0 | 0.2 | -0.5 | -0.6 | 0.2 | |
DS vs SF | -0.4 | -0.7 | -0.5 | -0.4 | -0.4 | -0.5 | |
ASD vs ASA | 0.4 | 0 | 0 | 0 | 0.4 | 0 | |
ASD vs | -0.2 | N/A | N/A | 0 | N/A | N/A | |
ASA vs | -0.3 | N/A | N/A | -0.2 | N/A | N/A | |
DS vs | -0.7 | N/A | N/A | -0.4 | N/A | N/A | |
SF vs | 0.5 | N/A | N/A | 0 | N/A | N/A | |
Cross-Correlations 1) | ZSD vs SF | 0 | 0 | 0 | 0 | 0 | 0 |
ZSA vs SF | 0 | 0 | 0 | -0.8 | -0.4 | 0 | |
ZSD vs K | 0 | N/A | N/A | 0 | N/A | N/A | |
ZSA vs K | 0 | N/A | N/A | 0 | N/A | N/A | |
ZSD vs DS | 0 | -0.5 | -0.6 | -0.2 | -0.5 | -0.6 | |
ZSA vs DS | 0.2 | 0 | -0.2 | 0 | 0 | -0.2 | |
ZSD vs ASD | 0.5 | 0.5 | -0.2 | 0.5 | 0.5 | -0.2 | |
ZSA vs ASD | 0.3 | 0.5 | 0 | 0 | -0.1 | 0 | |
ZSD vs ASA | 0 | 0 | 0 | -0.3 | 0 | 0 | |
ZSA vs ASA | 0 | 0.2 | 0.5 | 0.4 | 0 | 0.5 | |
ZSD vs ZSA | 0 | 0 | 0.5 | 0 | 0 | 0.5 | |
Delay scaling parameter rτ | 3 | 2.1 | 2.2 | 2.5 | 2.3 | 2.2 | |
XPR [dB] | μXPR | 9 | 8.0 | 9 | 8 | 7 | 9 |
σXPR | 3 | 3 | 5 | 4 | 3 | 5 | |
Number of clusters | 12 | 19 | 12 | 12 | 20 | 12 | |
Number of rays per cluster | 20 | 20 | 20 | 20 | 20 | 20 | |
Cluster DS ( | 5 | 11 | 11 | max(0.25, 6.5622 -3.4084 log10(fc)) | max(0.25, 6.5622 -3.4084 log10(fc)) | 11 | |
Cluster ASD ( | 3 | 10 | 5 | 3.58, see note 8 | 1.8, see note 8 | 1.8, see note 8 | |
Cluster ASA ( | 17 | 22 | 8 | 11 | 15 | 8 | |
Cluster ZSA ( | 7 | 7 | 3 | 7 | 7 | 3 | |
Per cluster shadowing std ζ [dB] | 3 | 3 | 4 | 3 | 3 | 4 | |
Correlation distance in the horizontal plane [m] | DS | 7 | 10 | 10 | 30 | 40 | 10 |
ASD | 8 | 10 | 11 | 18 | 50 | 11 | |
ASA | 8 | 9 | 17 | 15 | 50 | 17 | |
SF | 10 | 13 | 7 | 37 | 50 | 7 | |
| 15 | N/A | N/A | 12 | N/A | N/A | |
ZSA | 12 | 10 | 25 | 15 | 50 | 25 | |
ZSD | 12 | 10 | 25 | 15 | 50 | 25 | |
f c is carrier frequency in GHz; d2D is BS-UT distance in km. NOTE 1: DS = rms delay spread, ASD = rms azimuth spread of departure angles, ASA = rms azimuth spread of arrival angles, ZSD = rms zenith spread of departure angles, ZSA = rms zenith spread of arrival angles, SF = shadow fading, and K = Ricean K-factor. NOTE 2: The sign of the shadow fading is defined so that positive SF means more received power at UT than predicted by the path loss model. NOTE 3: All large scale parameters are assumed to have no correlation between different floors. NOTE 4: The following notation for mean (μlgX=mean{log10(X) }) and standard deviation (σlgX=std{log10(X) }) is used for logarithmized parameters X. NOTE 5: For all considered scenarios the AOD/AOA distributions are modelled by a wrapped Gaussian distribution, the ZOD/ZOA distributions are modelled by a Laplacian distribution and the delay distribution is modelled by an exponential distribution. NOTE 6: For UMa and frequencies below 6 GHz, use fc = 6 when determining the values of the frequency-dependent LSP values NOTE 7: For UMi and frequencies below 2 GHz, use fc = 2 when determining the values of the frequency-dependent LSP values NOTE 8: Value(s) marked with this note were updated as part of Release 19 Study on channel modelling enhancements for 7-24 GHz for NR [25]. NOTE 9: Value(s) were unchanged even after the considering measurements as part of Release 19 Study on channel modelling enhancements for 7-24 GHz for NR [25]. | |||||||
Table 7.5-6 Part-2: Channel model parameters for RMa (up to 7GHz) and Indoor-Office
Scenarios | RMa | Indoor-Office | ||||
LOS | NLOS | O2I | LOS | NLOS | ||
Delay spread (DS) lgDS=log10(DS/1s) | μlgDS | -7.49 | -7.43 | -7.47 | -0.01 log10(1+fc) - 7.692 | -0.28 log10(1+fc) - 7.173 |
σlgDS | 0.55 | 0.48 | 0.24 | 0.18 | 0.10 log10(1+fc) + 0.055 | |
AOD spread (ASD) lgASD=log10(ASD/1) | μlgASD | 0.90 | 0.95 | 0.67 | 1.60 | 1.62 |
σlgASD | 0.38 | 0.45 | 0.18 | 0.18 | 0.25 | |
AOA spread (ASA) lgASA=log10(ASA/1) | μlgASA | 1.52 | 1.52 | 1.66 | -0.19 log10(1+fc) + 1.781 | -0.11 log10(1+fc) + 1.863 |
σlgASA | 0.24 | 0.13 | 0.21 | 0.12 log10(1+fc) + 0.119 | 0.12 log10(1+fc) + 0.059 | |
ZOA spread (ZSA) lgZSA=log10(ZSA/1) | μlgZSA | 0.47 | 0.58 | 0.93 | -0.26 log10(1+fc) + 1.44 | -0.15 log10(1+fc) + 1.387 |
σlgZSA | 0.40 | 0.37 | 0.22 | -0.04 log10(1+fc) + 0.264 | -0.09 log10(1+fc) + 0.746 | |
Shadow fading (SF) [dB] | σSF | See Table 7.4.1-1 | 8 | See Table 7.4.1-1 | ||
K-factor (K) [dB] | μK | 7 | N/A | N/A | 7 | N/A |
σK | 4 | N/A | N/A | 4 | N/A | |
Cross-Correlations | ASD vs DS | 0 | -0.4 | 0 | 0.6 | 0.4 |
ASA vs DS | 0 | 0 | 0 | 0.8 | 0 | |
ASA vs SF | 0 | 0 | 0 | –0.5 | –0.4 | |
ASD vs SF | 0 | 0.6 | 0 | –0.4 | 0 | |
DS vs SF | -0.5 | -0.5 | 0 | –0.8 | –0.5 | |
ASD vs ASA | 0 | 0 | -0.7 | 0.4 | 0 | |
ASD vs | 0 | N/A | N/A | 0 | N/A | |
ASA vs | 0 | N/A | N/A | 0 | N/A | |
DS vs | 0 | N/A | N/A | -0.5 | N/A | |
SF vs | 0 | N/A | N/A | 0.5 | N/A | |
Cross-Correlations 1) | ZSD vs SF | 0.01 | -0.04 | 0 | 0.2 | 0 |
ZSA vs SF | -0.17 | -0.25 | 0 | 0.3 | 0 | |
ZSD vs K | 0 | N/A | N/A | 0 | N/A | |
ZSA vs K | -0.02 | N/A | N/A | 0.1 | N/A | |
ZSD vs DS | -0.05 | -0.10 | 0 | 0.1 | -0.27 | |
ZSA vs DS | 0.27 | -0.40 | 0 | 0.2 | -0.06 | |
ZSD vs ASD | 0.73 | 0.42 | 0.66 | 0.5 | 0.35 | |
ZSA vs ASD | -0.14 | -0.27 | 0.47 | 0 | 0.23 | |
ZSD vs ASA | -0.20 | -0.18 | -0.55 | 0 | -0.08 | |
ZSA vs ASA | 0.24 | 0.26 | -0.22 | 0.5 | 0.43 | |
ZSD vs ZSA | -0.07 | -0.27 | 0 | 0 | 0.42 | |
Delay scaling parameter rτ | 3.8 | 1.7 | 1.7 | 3.6 | 3 | |
XPR [dB] | μXPR | 12 | 7 | 7 | 11 | 10 |
σXPR | 4 | 3 | 3 | 4 | 4 | |
Number of clusters | 11 | 10 | 10 | 15 | 19 | |
Number of rays per cluster | 20 | 20 | 20 | 20 | 20 | |
Cluster DS ( | N/A | N/A | N/A | N/A | N/A | |
Cluster ASD ( | 2 | 2 | 2 | 5 | 5 | |
Cluster ASA ( | 3 | 3 | 3 | 8 | 11 | |
Cluster ZSA ( | 3 | 3 | 3 | 9 | 9 | |
Per cluster shadowing std ζ [dB] | 3 | 3 | 3 | 6 | 3 | |
Correlation distance in the horizontal plane [m] | DS | 50 | 36 | 36 | 8 | 5 |
ASD | 25 | 30 | 30 | 7 | 3 | |
ASA | 35 | 40 | 40 | 5 | 3 | |
SF | 37 | 120 | 120 | 10 | 6 | |
| 40 | N/A | N/A | 4 | N/A | |
ZSA | 15 | 50 | 50 | 4 | 4 | |
ZSD | 15 | 50 | 50 | 4 | 4 | |
fc is carrier frequency in GHz; d2D is BS-UT distance in km. NOTE 1: DS = rms delay spread, ASD = rms azimuth spread of departure angles, ASA = rms azimuth spread of arrival angles, ZSD = rms zenith spread of departure angles, ZSA = rms zenith spread of arrival angles, SF = shadow fading, and K = Ricean K-factor. NOTE 2: The sign of the shadow fading is defined so that positive SF means more received power at UT than predicted by the path loss model. NOTE 3: The following notation for mean (μlgX=mean{log10(X) }) and standard deviation (σlgX=std{log10(X) }) is used for logarithmized parameters X. NOTE 4: Void. NOTE 5: For all considered scenarios the AOD/AOA distributions are modelled by a wrapped Gaussian distribution, the ZOD/ZOA distributions are modelled by a Laplacian distribution and the delay distribution is modelled by an exponential distribution. NOTE 6: For InH and frequencies below 6 GHz, use fc = 6 when determining the values of the frequency-dependent LSP values | ||||||
) in [ns]
) in [deg]
) in [deg]
) in [deg]
Table 7.5-6 Part-3: Channel model parameters for InF
Scenarios | InF | ||
LOS | NLOS | ||
Delay spread (DS) lgDS=log10(DS/1s) 4) | μlgDS | log10(26(V/S)+14)-9.35 | log10(30(V/S)+32)-9.44
|
σlgDS | 0.15 | 0.19 | |
AOD spread (ASD) lgASD=log10(ASD/1) | μlgASD | 1.56 | 1.57 |
σlgASD | 0.25 | 0.2 | |
AOA spread (ASA) lgASA=log10(ASA/1) | μlgASA | -0.18*log10(1+fc) + 1.78 | 1.72 |
σlgASA | 0.12*log10(1+fc) + 0.2 | 0.3 | |
ZOA spread (ZSA) lgZSA=log10(ZSA/1) | μlgZSA | -0.2*log10(1+fc) + 1.5 | -0.13*log10(1+fc) + 1.45 |
σlgZSA | 0.35 | 0.45 | |
Shadow fading (SF) [dB] | σSF | Specified as part of path loss models | |
K-factor (K) [dB] | μK | 7 | N/A |
σK | 8 | N/A | |
Cross-Correlations | ASD vs DS | 0 | 0 |
ASA vs DS | 0 | 0 | |
ASA vs SF | 0 | 0 | |
ASD vs SF | 0 | 0 | |
DS vs SF | 0 | 0 | |
ASD vs ASA | 0 | 0 | |
ASD vs | -0.5 | N/A | |
ASA vs | 0 | N/A | |
DS vs | -0.7 | N/A | |
SF vs | 0 | N/A | |
Cross-Correlations 1) | ZSD vs SF | 0 | 0 |
ZSA vs SF | 0 | 0 | |
ZSD vs K | 0 | N/A | |
ZSA vs K | 0 | N/A | |
ZSD vs DS | 0 | 0 | |
ZSA vs DS | 0 | 0 | |
ZSD vs ASD | 0 | 0 | |
ZSA vs ASD | 0 | 0 | |
ZSD vs ASA | 0 | 0 | |
ZSA vs ASA | 0 | 0 | |
ZSD vs ZSA | 0 | 0 | |
Delay scaling parameter rτ | 2.7 | 3 | |
XPR [dB] | μXPR | 12 | 11 |
σXPR | 6 | 6 | |
Number of clusters | 25 | 25 | |
Number of rays per cluster | 20 | 20 | |
Cluster DS ( | N/A | N/A | |
Cluster ASD ( | 5 | 5 | |
Cluster ASA ( | 8 | 8 | |
Cluster ZSA ( | 9 | 9 | |
Per cluster shadowing std ζ [dB] | 4 | 3 | |
Correlation distance in the horizontal plane [m] 5) | DS | 10 | 10 |
ASD | 10 | 10 | |
ASA | 10 | 10 | |
SF | 10 | 10 | |
| 10 | N/A | |
ZSA | 10 | 10 | |
ZSD | 10 | 10 | |
fc is carrier frequency in GHz; d2D is BS-UT distance in km. NOTE 1: DS = rms delay spread, ASD = rms azimuth spread of departure angles, ASA = rms azimuth spread of arrival angles, ZSD = rms zenith spread of departure angles, ZSA = rms zenith spread of arrival angles, SF = shadow fading, and K = Ricean K-factor. NOTE 2: The sign of the shadow fading is defined so that positive SF means more received power at UT than predicted by the path loss model. NOTE 3: The following notation for mean (μlgX=mean{log10(X) }) and standard deviation (σlgX=std{log10(X) }) is used for logarithmized parameters X. NOTE 4: V = hall volume in m3, S = total surface area of hall in m2 (walls+floor+ceiling) NOTE 5: When the simulation scenario is small it is recommended to consider methods to improve the statistical confidence, such as using multiple random seeds in simulations. | |||
Table 7.5-6 Part-4: Channel model parameters for SMa (up to 24 GHz)
Scenarios | SMa | |||
LOS | NLOS | O2I | ||
Delay spread (DS) lgDS=log10(DS/1s) | μlgDS | –7.42 | -7.20 | -7.20 |
σlgDS | 0.60 | 0.58 | 0.58 | |
AOD spread (ASD) lgASD=log10(ASD/1) | μlgASD | 0.48 | 0.51 | 0.51 |
σlgASD | 0.27 | 0.33 | 0.33 | |
AOA spread (ASA) lgASA=log10(ASA/1) | μlgASA | 1.43 | 1.63 | 1.63 |
σlgASA | 0.12 | 0.26 | 0.26 | |
ZOA spread (ZSA) lgZSA=log10(ZSA/1) | μlgZSA | 1.18 | 1.16 | 1.16 |
σlgZSA | 0.05 | 0.14 | 0.14 | |
Shadow fading (SF) [dB] | σSF | See Table 7.4.1-1 | See Table 7.4.1-1 | 8 |
K-factor (K) [dB] | μK | 9 | N/A | N/A |
σK | 7 | N/A | N/A | |
Cross-Correlations | ASD vs DS | 0.4 | 0 | 0 |
ASA vs DS | 0.8 | 0.7 | 0.7 | |
ASA vs SF | –0.5 | 0 | 0 | |
ASD vs SF | –0.5 | -0.4 | -0.4 | |
DS vs SF | –0.4 | -0.13 | -0.13 | |
ASD vs ASA | 0 | 0 | 0 | |
ASD vs | 0 | N/A | N/A | |
ASA vs | -0.2 | N/A | N/A | |
DS vs | -0.4 | N/A | N/A | |
SF vs | 0 | N/A | N/A | |
Cross-Correlations 1) | ZSD vs SF | 0 | 0 | 0 |
ZSA vs SF | -0.8 | -0.47 | -0.47 | |
ZSD vs K | 0 | N/A | N/A | |
ZSA vs K | 0 | N/A | N/A | |
ZSD vs DS | -0.2 | -0.5 | -0.5 | |
ZSA vs DS | 0 | 0 | 0 | |
ZSD vs ASD | 0.5 | 0.5 | 0.5 | |
ZSA vs ASD | 0 | -0.1 | -0.1 | |
ZSD vs ASA | -0.3 | 0 | 0 | |
ZSA vs ASA | 0.4 | 0.36 | 0.36 | |
ZSD vs ZSA | 0 | 0 | 0 | |
Delay scaling parameter rτ | 2.4 | 1.5 | 1.5 | |
XPR [dB] | µXPR | 8 | 4 | 4 |
σXPR | 4 | 3 | 3 | |
Number of clusters N | 15 | 14 | 14 | |
Number of rays per cluster M | 20 | 20 | 20 | |
Cluster DS ( | max(0.25, 6.5622 -3.4084 log10(fc)) | max(0.25, 6.5622 -3.4084 log10(fc)) | max(0.25, 6.5622 -3.4084 log10(fc)) | |
Cluster ASD ( | 2.08 | 1.33 | 1.33 | |
Cluster ASA ( | 5 | 10 | 10 | |
Cluster ZSA ( | 7 | 7 | 7 | |
Per cluster shadowing std ζ [dB] | 3 | 3 | 3 | |
Correlation distance in the horizontal plane [m] | DS | 6 | 40 | 40 |
ASD | 15 | 30 | 30 | |
ASA | 20 | 30 | 30 | |
SF | 40 | 50 | 50 | |
K | 10 | N/A | N/A | |
ZSA | 15 | 50 | 50 | |
ZSD | 15 | 50 | 50 | |
f c is carrier frequency in GHz; d2D is BS-UT distance in km. NOTE 1: DS = rms delay spread, ASD = rms azimuth spread of departure angles, ASA = rms azimuth spread of arrival angles, ZSD = rms zenith spread of departure angles, ZSA = rms zenith spread of arrival angles, SF = shadow fading, and K = Ricean K-factor. NOTE 2: The sign of the shadow fading is defined so that positive SF means more received power at UT than predicted by the path loss model. NOTE 3: The following notation for mean (μlgX=mean{log10(X) }) and standard deviation (σlgX=std{log10(X) }) is used for logarithmized parameters X. NOTE 4: Void. NOTE 5: For all considered scenarios the AOD/AOA distributions are modelled by a wrapped Gaussian distribution, the ZOD/ZOA distributions are modelled by a Laplacian distribution and the delay distribution is modelled by an exponential distribution. | ||||
Table 7.5-7: ZSD and ZOD offset parameters for UMa
Scenarios | LOS | NLOS | |
ZOD spread (ZSD) lgZSD=log10(ZSD/1) | μlgZSD | max[-0.5, -2.1(d2D/1000) -0.01 (hUT - 1.5)+0.75] | max[-0.5, -2.1(d2D/1000) -0.01(hUT - 1.5)+0.9] |
σlgZSD | 0.40 | 0.49 | |
ZOD offset | µoffset,ZOD | 0 | e(fc)-10^{a(fc) log10(max(b(fc), d2D)) +c(fc) -0.07(hUT-1.5)} |
Note: For NLOS ZOD offset: a(fc) = 0.208log10(fc)- 0.782; b(fc) = 25; c(fc) = -0.13log10(fc)+2.03; e(fc) = 7.66log10(fc)-5.96. | |||
Table 7.5-8: ZSD and ZOD offset parameters for UMi – Street Canyon
Scenarios | LOS | NLOS | |
ZOD spread (ZSD) lgZSD=log10(ZSD/1) | μlgZSD | max[-0.21, -14.8(d2D/1000) + 0.01|hUT-hBS| + 0.83] | max[-0.5, -3.1(d2D/1000) + 0.01 max(hUT-hBS,0) +0.2] |
σlgZSD | 0.35 | 0.35 | |
ZOD offset | µoffset,ZOD | 0 | -10^{-1.5log10(max(10, d2D))+3.3} |
Table 7.5-9: ZSD and ZOD offset parameters for RMa
Scenarios | LOS | NLOS | O2I | |
ZOD spread (ZSD) lgZSD=log10(ZSD/1) | μlgZSD | max[-1, -0.17(d2D/1000) -0.01(hUT - 1.5) + 0.22] | max[-1, -0.19(d2D/1000) -0.01(hUT - 1.5) + 0.28] | max[-1, -0.19(d2D/1000) -0.01(hUT - 1.5) + 0.28] |
σlgZSD | 0.34 | 0.30 | 0.30 | |
ZOD offset | µoffset,ZOD | 0 | arctan((35 - 3.5)/d2D) -arctan((35 - 1.5)/d2D) | arctan((35 - 3.5)/d2D) -arctan((35 - 1.5)/d2D) |
Table 7.5-10: ZSD and ZOD offset parameters for Indoor-Office
Scenarios | LOS | NLOS | |
ZOD spread (ZSD) lgZSD=log10(ZSD/1) | μlgZSD | -1.43 log10(1+ fc)+2.228 | 1.08 |
σlgZSD | 0.13 log10(1+fc)+0.30 | 0.36 | |
ZOD offset | µoffset,ZOD | 0 | 0 |
Table 7.5-11: ZSD and ZOD offset parameters for InF
Scenarios | LOS | NLOS | |
ZOD spread (ZSD) lgZSD=log10(ZSD/1) | μlgZSD | 1.35 | 1.2 |
σlgZSD | 0.35 | 0.55 | |
ZOD offset | µoffset,ZOD | 0 | 0 |
Table 7.5-12: ZSD and ZOD offset parameters for SMa
Scenarios | LOS | NLOS | O2I | |
ZOD spread (ZSD) lgZSD=log10(ZSD/1) | μlgZSD | 0.14 | 0.14 | 0.14 |
σlgZSD | 0.16 | 0.16 | 0.16 | |
ZOD offset | µoffset,ZOD | 0 | ||
Notes for Tables 7.5-7, 7.5-8, 7.5-9, 7.5-10, 7.5-11, 7.5-12:
NOTE 1: fc is center frequency in GHz; d2D is BS-UT distance in m.
NOTE 2: hBS and hUT are antenna heights in m for BS and UT respectively.
NOTE 3: The following notation for mean (μlgX=mean{log10(X) }) and standard deviation (σlgX=std{log10(X)}) is used for logarithmized parameters X.
NOTE 4: For frequencies below 6 GHz, use fc = 6 when determining the values of the frequency-dependent ZSD and ZOD offset parameters in Table 7.5-7 and 7.5-10
NOTE 5: The ZSD parameters for O2I links are the same parameters that are used for outdoor links, depending on the LOS condition of the outdoor link part in Tables 7.5-7 and 7.5-8.
7.6 Additional modelling components #
7.6.0 Introduction of additional modelling components #
The additional modelling components in this clause are to support advanced simulations, such as simulations with very large arrays and large bandwidth, simulations affected by oxygen absorption (frequencies between 53 and 67 GHz), simulations in which spatial consistency is important (e.g. high number of closely located user), simulations of mobility, simulations of blockage effects, simulations of absolute time of arrival, simulations of dual mobility, simulations that include sources of EM interference, simulations of devices embedded in machinery or enclosures, simulations of near-field channel effects (i.e. characteristics of spherical wavefront), simulations of spatial non-stationarity (i.e. antenna element-wise power variation), simulation of number of cluster variability, and simulation of polarization power variability. These modelling components affect some of the steps between Step 1 and Step 12 in Clause 7.5.
These extensions are computationally more expensive and might not be required in all evaluation cases.
7.6.1 Oxygen absorption #
Oxygen absorption loss is applied to the cluster responses generated in Step 11 in Clause 7.5. The additional loss, OLn(fc) for cluster n at centre frequency fc is modelled as:
[dB] (7.6-1)
where:
- α(fc) is frequency dependent oxygen loss [dB/km] characterized in Table 7.6.1-1;
- c is the speed of light [m/s]; and d3D is the distance [m];
- τn is the n-th cluster delay [s] in Step 11 in Clause 7.5;
- τΔ is 0 in the LOS case and min(
) otherwise, where min() is the minimum delay in Step 5.
For centre frequencies not specified in this table, the frequency dependent oxygen loss α(fc) is obtained from a linear interpolation between two loss values corresponding to the two adjacent centre frequencies of the centre frequency fc.
Table 7.6.1-1 Frequency dependent oxygen loss α(f) [dB/km]
f in [GHz] | 0-52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68-100 |
α(f) in [dB/km] | 0 | 1 | 2.2 | 4 | 6.6 | 9.7 | 12.6 | 14.6 | 15 | 14.6 | 14.3 | 10.5 | 6.8 | 3.9 | 1.9 | 1 | 0 |
For large channel bandwidth, first transform the time-domain channel response of each cluster (all rays within one cluster share common oxygen absorption loss for simplicity) into frequency-domain channel response, and apply the oxygen absorption loss to the cluster's frequency-domain channel response for frequency fc + Δf within the considered bandwidth. The oxygen loss, OLn(fc+ Δf) for cluster n at frequency fc+ Δf is modelled as:
[dB] (7.6-2)
where:
- α(fc+ Δf) is the oxygen loss (dB/km) at frequency fc+ Δf characterized in Table 7.6.1-1. Note that Δf is in [-B/2, B/2], where B is the bandwidth. Linear interpolation is applied for frequencies not provided in Table 7.6.1-1.
The final frequency-domain channel response is obtained by the summation of frequency-domain channel responses of all clusters.
Time-domain channel response is obtained by the reverse transform from the obtained frequency-domain channel response.
7.6.2 Large bandwidth and large antenna array #
7.6 .2.1 Modelling of the propagation delay #
The modelling in this clause applies only when the bandwidth B is greater than c/D Hz, where
- D is the maximum antenna aperture in either azimuth or elevation (m)
- c is the speed of light (m/s)
Each ray within a cluster for a given u (Rx) and s (Tx) has unique time of arrival (TOA). The channel coefficient generation step (Step 11 in Clause 7.5) is updated to model individual rays. In this case, the channel response of ray m in cluster n for a link between Rx antenna u and Tx antenna s at delay 
at time t is given by:
(7.6-3)
with 
is the wavelength on frequency 
, which can be implemented by user's own method. The delay (TOA) for ray m in cluster n for a link between Rx antenna u and Tx antenna s is given by:
(7.6-4)
Note that Equation (7.6-3) only considers the delays 
intentionally. If unequal ray powers are considered, 
are generated according to Clause 7.6.2.2. Otherwise, ray powers are equal within a cluster, i.e., 
for all m. Note: this model is developed assuming plane wave propagation.
7.6 .2.2 Modelling of intra-cluster angular and delay spreads #
With large antenna arrays or large bandwidths, the angle and/or delay resolution can be larger than what the fast fading model in Clause 7.5 is designed to support. To model this effect, the following modifications to Step 7 in Clause 7.5 can be optionally used.
1. The offset angles αm in (7.5-13), (7.5-18) and (7.5-20) are generated independently per cluster and ray using:
(7.6-5)
where 
denotes the continuous uniform distribution in the interval 
. These random variables may further be modelled as spatially consistent with correlation distance equal to the cluster-specific random variable correlation distance of Table 7.6.3.1-2.
2. The relative delay of m-th ray is given by 
, where n is the cluster index, 
, the cluster DS 
is given in Table 7.5-6. These random variables may further be modelled as spatially consistent with correlation distance equal to the cluster-specific random variable correlation distance of Table 7.6.3.1-2. In this case, the sub-cluster mapping according to (7.5-26) and Table 7.5-5 shall not be applied. The delays to be used in Equation (7.6-3) are given by 
.
3. Ray powers are determined unequally by the following process:
The power of m-th ray in n-th cluster is given by 
for m = 1,…,M, where
(7.6-6)
and 
, 
, 
, and 
are respectively the intra-cluster delay spread and the corresponding intra-cluster angular spread that are given in Table 7.5-6. The cluster zenith spread of departure is given by
, (7.6-7)
with 
being defined in Tables 7.5-7, 7.5-8, 7.5-9, 7.5-10 and 7.5-11.
4. The number of rays per cluster shall be calculated as follows:
where:
- 
- 
- 
-
- 
is the upper limit of 
, and it should be selected by the user of channel model based on the trade-off between simulation complexity and accuracy.
- 
and 
are the array size in m in horizontal and vertical dimension, B is bandwidth in Hz, 
and 
are the cluster spreads in degrees, and 
is the wavelength.
- 
is a "sparseness" parameter with value 0.5.
It is noted that each MPC may have different AOD, ZOD, and delay.
7.6 .3 Spatial consistency #
7.6 .3.1 Spatial consistency procedure #
A new procedure, namely a spatial consistency procedure, can be used for both cluster-specific and ray-specific random variables to be generated in various channel generation steps in Clause 7.5, so that they are spatially consistent for drop-based simulations. Alternatively, this can be used together with Procedure B described in Clause 7.6.3.2 for spatially consistent mobility simulations.
The procedure can be considered as a 2D random process (in the horizontal plane) given the UT locations based on the parameter-specific correlation distance values for spatial consistency, specified in Table 7.6.3.1-2. The cluster specific random variables include:
- Cluster specific random delay in Step 5;
- Cluster specific shadowing in Step 6; and
- Cluster specific offset for AOD/AOA/ZOD/ZOA in Step 7.
- Cluster specific sign for AOD/AOA/ZOD/ZOA in Step 7.
- Optionally in case of large bandwidth as described in Clause 7.6.2.2 the procedure may apply as well for the parameters of rays within a cluster.
The procedure shall apply to each cluster before sorting the delay. Cluster specific sign for AOD/AOA/ZOD/ZOA in Step 7 shall be kept unchanged per simulation drop even if UT position changes during simulation. The ray specific random variables include:
- Random coupling of rays in Step 8;
- XPR in Step 9; and
- Random phase in Step 10.
The random coupling of rays in Step 8 and the intra-cluster delays in Step 11 shall be kept unchanged per simulation drop even if UT position changes during simulation.
Table 7.6.3.1-2 Correlation distance for spatial consistency
Correlation distance in [m] | RMa | UMi | UMa | SMa | Indoor | InF | ||||||||
LOS | NLOS | O2I | LOS | NLOS | O2I | LOS | NLOS | O2I | LOS | NLOS | O2I | |||
Cluster and ray specific random variables | 50 | 60 | 15 | 12 | 15 | 15 | 40 | 50 | 15 | 40 | 50 | 15 | 10 | 10 |
LOS/NLOS state | 60 | 50 | 50 | 50 | 10 | |||||||||
Indoor/outdoor state | 50 | 50 | 50 | 50 | N/A | N/A | ||||||||
7.6.3.2 Spatially-consistent UT/BS mobility modelling #
For mobility simulation enhancement, two alternative spatial consistency procedures – Procedure A and Procedure B – are described as follows. The procedures presented below consider the downlink direction same as in Clause 7.5.
Procedure A:
For 
when a UT/BS is dropped into the network, spatially consistent powers/delays/angles of clusters are generated according to Clause 7.6.3.1.
The updated distance of UT/BS should be limited within 1 meter, i.e. when 
, and the updated procedure in the following should take the closest realization instead of . In case the absolute time of arrival modeling of clause 7.6.9 is simultaneously used, the update distance should be the minimum of 1 meter and
At 
, update channel cluster power/delay/angles based on UT/BS channel cluster power/delay/angles, moving speed moving direction and UT position at 
.
Cluster delay is updated as:
where
is the speed of light in [m/s];
is the 3D distance between Rx antenna and Tx antenna at 
in [m];
are the cluster delays in [s] as in Step 11 in Clause 7.5;
, where 
are the delays from Equation (7.5-1). In case the absolute time of arrival modelling of clause 7.6.9 is simultaneously used, 
where
Also,
(7.6-10)
where 
and 
are cluster specific zenith and azimuth angles of arrival.
where
After updating the delays according to equation (7.6-9), the delays over the mobility range are normalized. Equation (7.5-2) of the fast fading model is replaced by
(7.6-10a)
in which 
covers the entire duration of the mobility model.
Cluster powers are updated as in Step 6 using the cluster delays from Equation (7.6-10a).
Cluster departure angles (
and 
) and arrival angles (
and 
) are updated using a cluster-wise transformation of the UT/BS velocity vector given by
with
and
where 
and 
being the rotation matrices around y-axis and z-axis as defined in Equation (7.1-2). Random variable 
is sampled from a uniform distribution on a NLOS cluster basis and is not changed during the UT mobility within a drop. The cluster specific decorrelation distances are 60m, 15m, 50m and 10m for RMa, UMi, UMa and Indoor scenarios, respectively.
Now, the departure and arrival angles in radians are updated as:
and
with 
and 
being the spherical unit vectors defined in Equations (7.1-13) and (7.1-14).
Procedure B:
In procedure B, spatial or time evolution of the channel is obtained by generating channel realizations separately for all links to different Rx positions using Steps 1-12 of Clause 7.5 together with the spatially consistent procedure of Clause 7.6.3.1. In the case of mobility these positions may be a function of time along one or more Rx trajectories. Furthermore, to ensure that the spatial or time evolution of delays and angles are within reasonable limits, Steps 5, 6, and 7 in Clause 7.5 should be replaced by the below procedure.
Note: For implementation purposes, LSPs and SSPs may be interpolated within the coherence length or time of the respective parameter.
Step 5: Generate cluster delays 
, with 
.
delays are drawn randomly from a uniform distribution.
(7.6-15)
Normalise the delays by subtracting the minimum delay: 
. The autocorrelation distance for 
is 
.
In the case of LOS, set the delay of the first cluster 
to 0.
Step 6: Generate arrival angles and departure angles for both azimuth and elevation using (7.6-16) instead of (7.5-9) and (7.5-14).
(7.6-16)
with . This step is repeated independently for AOD, AOA, ZOD, and ZOA with corresponding maximum angles for the uniform distribution. In case of LOS, set the angles of the first cluster (
, etc) to 0.
The autocorrelation distances are for AOD, and ZOD, while they are fixed to 50m for AOA, and ZOA.
Offset angles etc are applied in the modified Step 7b below after cluster powers have been calculated.
Step 7: Generate cluster powers
Cluster powers are calculated assuming a single slope exponential power profile and Laplacian angular power profiles. The cluster powers are determined by
(7.6-17)
where 
(autocorrelation distance same as for shadow fading) is the per cluster shadowing term in [dB]. Delay spread 
and angular spreads 
, 
, 
, 
are generated in Step 4 of Clause 7.5. Normalize the cluster powers so that the sum of all cluster powers is equal to one, i.e.,
(7.6-17a)
In the case of LOS condition, substitute with 
and {, , , } with {
, 
, 
, 
} respectively to preserve the delay and angular spreads. 
is the Ricean K-factor as generated in Step 4 converted to linear scale. Furthermore, an additional specular component is added to the first cluster. Power of the single LOS ray is:
(7.6-17b)
and the cluster powers are not normalized as in equation (7.6-17a), but:
(7.6-17c)
where δ(.) is Dirac's delta function.
Assign the power of each ray within a cluster as Pn / M, where M is the number of rays per cluster.
Step 7b: Apply offset angles
The ray AOA angles are determined by
(7.6-17d)
where 
is the LOS direction defined in the network layout description, see Step1c.
The generation of AOD () follows a procedure similar to AOA as described above.
The ZOA angles are determined by
(7.6-17e)
where 
if the BS-UT link is O2I and otherwise. The LOS direction is defined in the network layout description, see Step1c.
The ZOA angles are determined by
(7.6-17f)
where is the mean of the ZSD log-normal distribution.
Some of the delay and angle spreads and standard deviations used in equations (7.6-15) and (7.6-16) may be frequency-dependent. In the case of multi-frequency simulations according to Clause 7.6.5, the largest value among all the simulated frequencies should be used in (7.6-15) and (7.6-16) so that the cluster and ray delays and angles (but not the powers or the resulting delay or angular spreads) are the same for all frequencies.
7.6 .3.3 LOS/NLOS, indoor states and O2I parameters #
The LOS state can be determined according to the spatial consistency procedure for random variables as mentioned in Clause 7.6.3.1, by comparing a realization of a random variable generated with distance-dependent LOS probability. If the realization is less than the LOS probability, the state is LOS; otherwise NLOS. Decision of LOS and NLOS status should be used in Step 2 in Clause 7.5 if this advanced simulation is performed.
The same procedure can be applied for determining the indoor state, with the indoor probability instead of the LOS probability.
The correlation distance for LOS state and indoor/outdoor is specified in Table 7.6.3.1-2.
The indoor distance can be modeled as the minimum of two spatially consistent uniform random variables within (0, 25) meters with correlation distance 25m.
Note in case the UT is in an indoor state, the pathloss model changes and a penetration loss is considered. For details on the model, see Clause 7.4.3. Here, the focus is on modelling aspects with respect to spatial consistency. As described in Clause 7.4.3, the penetration loss deviation σp represents variations within and between buildings of the same type. For spatial consistency this can be modeled as a spatially consistent random variable with correlation distance 10m, see Clause 7.6.3.1. The "building type" is determined using a spatially consistent uniform random variable with correlation distance 50 m. The building type is determined by comparing the random variable with P1, where P1 is the probability of the building type with low loss penetration. If the realization of the random variable is less than P1, the building type is low loss; otherwise the building type is high loss.
The cluster-specific and ray-specific random variables as defined in Clause 7.6.3.1 on the same floor are generated in the spatial consistency modelling; otherwise, these variables across different floors are uncorrelated.
In case there is a transition from LOS to NLOS due to UT mobility, there will be a hard transition in the channel response. This is because pathloss and LS parameters are different for these states, leading automatically to very different channel realizations. To circumvent such hard transitions the optional soft LOS state can be considered to determine the PL and the channel impulse responses containing characteristics of both LOS and NLOS. Soft LOS state 
is generated by floating numbers between 0 (NLOS) and 1 (LOS) in the spatial consistency modelling. The value of is determined by
, (7.6-18)
- 
is a spatially consistent Gaussian random variable with correlation distance according to Table 7.6.3.1-2;
- 
- 
After is obtained, Steps 2-12 of the channel coefficient generation described in Clause 7.5 are performed twice, once with the propagation condition in Step 2 set as LOS and once with the propagation condition in Step 2 set as NLOS. The resulting channel coefficients are denoted as 
and 
respectively, where is generated with the LOS path loss formula and channel model parameters while is 
generated using the NLOS path loss formula and channel model parameters. The channel matrix 
with soft LOS state is determined from a linear combination of and as:
(7.6-19)
It is noted that soft indoor/outdoor states are not modelled in this TR. Thus the model does not support transitions between indoor/outdoor states in mobility simulations.
7.6.3.4 Applicability of spatial consistency #
According to the characteristics of different cluster specific parameters and ray specific parameters, the following types on spatial consistency are defined:
- Site-specific: parameters for different BS-UT links are uncorrelated, but the parameters for links from co-sited sectors to a UT are correlated.
- All-correlated: BS-UT links are correlated.
In Table 7.6.3.4-1, correlation type for each large scale parameter, cluster specific parameter and ray specific parameter is clarified.
Table 7.6.3.4-1: Correlation type among TRPs
Parameters | Correlation type |
Delays | Site-specific |
Cluster powers | Site-specific |
AOA/ZOA/AOD/ZOD offset | Site-specific |
AOA/ZOA/AOD/ZOD sign | Site-specific |
Random coupling | Site-specific |
XPR | Site-specific |
Initial random phase | Site-specific |
LOS/NLOS states | Site-specific |
Blockage (Model A) | All-correlated |
O2I penetration loss | All-correlated |
Indoor distance | All-correlated |
Indoor states | All-correlated |
Spatial consistency is not modelled in the following situations:
- Different link types, e.g., outdoor LOS, outdoor NLOS or O2I
- UE locates on different floors
Table 7.6.3.4-2, lists conditions in which each large scale parameter, cluster specific parameter and ray specific parameter is assumed not spatially correlated.
Table 7.6.3.4-2: Conditions when parameters are not spatially correlated.
Parameters | Uncorrelated states |
Delays | Outdoor LOS/outdoor NLOS/O2I (different floors) |
Cluster powers | Outdoor LOS/outdoor NLOS/O2I (different floors) |
AOA/ZOA/AOD/ZOD offset | Outdoor LOS/outdoor NLOS/O2I (different floors) |
AOA/ZOA/AOD/ZOD sign | Outdoor LOS/outdoor NLOS/O2I (different floors) |
Random coupling | Outdoor LOS/outdoor NLOS/O2I (different floors) |
XPR | Outdoor LOS/outdoor NLOS/O2I (different floors) |
Initial random phase | Outdoor LOS/outdoor NLOS/O2I (different floors) |
Blockage | Outdoor/O2I (different floors) |
Standard deviation for O2I penetration loss | Different building types, i.e., high/low loss |
7.6 .4 Blockage #
Blockage modelling is an add-on feature to the channel model. The method described in the following applies only when this feature is turned on. In addition, the temporal variability of the blockage modelling parameters is on-demand basis. It is also noted that the modelling of the blockage does not change LOS/NLOS state of each link.
When blockage model is applied, the channel generation in Clause 7.5 should have several additional steps between Step 9 and 10 as illustrated in Figure 7.6.4-1.
Figure.7.6.4-1 Channel generation procedure with blockage model
Two alternative models (Model A and Model B) are provided for the blockage modelling. Both approaches have their own use cases. Model A is applicable when a generic and computationally efficient blockage modelling is desired. Model B is applicable when a specific and more realistic blocking modelling is desired.
7.6 .4.1 Blockage model A #
Model A adopts a stochastic method for capturing human and vehicular blocking.
Step a: Determine the number of blockers.
Multiple 2-dimensional (2D) angular blocking regions, in terms of centre angle, azimuth and elevation angular span are generated around the UT. There is one self-blocking region, and
Step b: Generate the size and location of each blocker.
For self-blocking, the blocking region in UT LCS is defined in terms of elevation and azimuth angles, (
,
) and azimuth and elevation angular span (
, 
).
(7.6-20)
where the parameters are described in Table 7.6.4.1-1.
Table 7.6.4.1-1: Self-blocking region parameters.
|
|
|
|
|
Portrait mode | 260o | 120o | 100o | 80o |
Landscape mode | 40o | 160o | 110o | 75o |
For non-self-blocking k = 1, …, 4, the blocking region in GCS is defined by
(7.6-21)
where the parameters are described in Table 7.6.4.1-2, as well as the distance r between the UT and the blocker.
Table 7.6.4.1-2: Blocking region parameters.
Blocker index (k = 1, …, 4) |
|
|
|
| r |
InH scenario | Uniform in [0o, 360o] | Uniform in [15o, 45o] | 90o | Uniform in [5o, 15o] | 2 m |
UMi, UMa, SMa, RMa scenarios | Uniform in [0o, 360o] | Uniform in [5o, 15o] | 90o | 5o | 10 m |
Step c: Determine the attenuation of each cluster due to blockers.
The attenuation of each cluster due to self-blocking corresponding to the centre angle pair (
,
), is 30 dB provided that 
and 
. Otherwise, the attenuation is 0 dB.
The attenuation of each cluster due to the non-self-blocking regions (k=1, …, 4) is given by
(7.6-22)
provided that 
and 
. Otherwise, the attenuation is 0 dB. The terms in the above equation are given as
(7.6-23)
where
, (7.6-24)
, (7.6-25)
, (7.6-26)
(7.6-27)
In the above formula for 
, 
is the wavelength. The appropriate signs (
)within the 
term are described in Table 7.6.4.1-3.
Table 7.6.4.1-3: Description of signs
|
|
|
|
| (-, +) for (+, -) for | (-, +) for (+, +) for | (-, +) for (-, +) for |
| (+, +) for (+, -) for | (+, +) for (+, +) for | (+, +) for (-, +) for |
| (+, -) for (+, -) for | (+, -) for (+, +) for | (+, -) for (-, +) for |
Step d: Spatial and temporal consistency of each blocker.
The centre of the blocker is generated based on a uniformly distributed random variable, which is temporally and spatially consistent. The two-dimensional autocorrelation function 
can be described with sufficient accuracy by the exponential function
(7.6-28)
The spatial correlation distance 
for the random variable determining the centre of the blocker is given in Table 7.6.4.1-4 for different scenarios.
Table 7.6.4.1-4: Spatial correlation distance for different scenarios.
Spatial correlation distance | UMi/UMa/SMa/RMa | InH | |||
LOS | NLOS | O2I | LOS | NLOS | |
10 | 10 | 5 | 5 | 5 | |
The correlation time
, where 
is the speed of the moving blocker.
Note: The rectangular blocker description is chosen for self-blocking region with the specific choices of 
, 
) assumed here. Generalization of this description to other choices should be done with care as the rectangular description may not be accurate.
7.6 .4.2 Blockage model B #
Model B adopts a geometric method for capturing e.g., human and vehicular blocking.
Step a: Determine blockers
A number, 
) and width (
), with the screen centre at coordinate 
.
Note:
- The number of blockers (
and 
), locations 
, density, and movement pattern (if non-stationary) are all simulation assumptions, to allow different blocking scenarios to be constructed depending on the need of the particular simulation study.
Recommended parameters for typical blockers are provided in Table 7.6.4.2-5.
- The blocking effect diminishes with increasing distance to the blocker. For implementation purposes it may be sufficient to consider only the
Table 7.6.4.2-5: Recommended blocker parameters
| Typical set of blockers | Blocker dimensions | Mobility pattern |
Indoor; Outdoor; InF | Human | Cartesian: w=0.3m; h=1.7m | Stationary or up to 3 km/h |
Outdoor | Vehicle | Cartesian: w=4.8m; h=1.4m | Stationary or up to 100 km/h |
InF | AGV | Cartesian: w=3m; h=1.5m | Up to 30 km/h |
InF | Industrial robot | Cartesian: w=2m; h=0.2m | Up to 3 m/s |
Step b: Determine the blockage attenuation per sub-path
Attenuation caused by each blocker to each sub-path is modelled using a simple knife edge diffraction model and is given by
(7.6-29)
where 
, 
and 
, 
account for knife edge diffraction at the four edges, and are given by
(7.6-30)
where 
is the wave length. As shown in Figure 7.6.4.2-2, 
are the projected (onto the side and top view planes) distances between the receiver and four edges of the corresponding blocker, and 
are the projected (onto the side and top view planes) distances between the transmitter and four edges of the corresponding blocker. The side view plane is perpendicular to the horizontal ground plane. The top view is perpendicular to the side view. For each cluster, the blocker screen is rotated around its centre such that the arrival direction of the corresponding path is always perpendicular to the screen. It should be noted that different rotations are required for each individual sub-path. Meanwhile, the base and top edges of the screens are always parallel to the horizontal plane. As the screen is perpendicular to each sub-path, 
is the distance between the transmitter and receiver for the direct path in LOS, and 
is the distance between the blocker screen and receiver, projected onto the incoming sub-path direction, for all the other (NLOS) paths. In the equation of 
, the plus and minus signs are determined in such a way that, as shown in Figure 7.6.4.2-2,
- if the sub-path (terminated at the receiver or transmitter) does not intersect the screen in side view, minus sign is applied for the shortest path among 
and 
in the NLOS case (
and 
in the LOS case) and plus sign is applied for the other edge.
- if the sub-path (terminated at the receiver or transmitter) does not intersect the screen in top view, minus sign is applied for the shortest path among 
and 
in the NLOS case (
and 
for the LOS case) and plus sign is applied for the other edge.
- if the sub-path intersects the screen plus signs are applied for both edges.
For the case of multiple screens the total loss is given by summing the losses of each contributing screen in dB units.
The model according to option B is consistent in time, frequency and space, and is more appropriate to be used for simulations with arbitrarily designated blocker density.
Figure 7.6.4.2-2(a): Illustration of the geometric relation among blocker, receiver and transmitter for the LOS path
Figure 7.6.4.2-2(b): Illustration of the geometric relation between blocker and receiver for NLOS paths
7.6 .5 Correlation modelling for multi-frequency simulations #
This clause describes how to generate parameters to reflect correlation across different frequencies for a BS-UT link, for simulations involved with multiple frequencies.
For those simulations, the steps in Clause 7.5 should be revised according to the following:
- The parameters generated in Step 1 are the same for all the frequencies, except for antenna patterns, array geometries, system center frequency and bandwidth.
- Propagation conditions generated in Step 2 are the same for all the frequencies. It is noted that soft LOS states may be different due to frequency dependent function.
- The parameters generated in Step 4 are the same for all the frequencies, except for possibly frequency-dependent scaling of e.g. delay spread and angular spreads according to the LSP tables. I.e. let x be a random variable drawn from a Gaussian distribution: x ~ N(0,1). Then the delay spread at frequency f is DS(f)= 10^(μlgDS(f) + σlgDS(f)x), where the same value of x is used for all frequencies. A corresponding procedure applies to each of the angular spreads.
- The cluster delays and angles resulting from Steps 5-7 are the same for all frequency bands
- Per-cluster shadowing n in Step 6 are independently generated for the frequency bands.
- Cluster powers in Step 6 may be frequency-dependent.
- Steps 8-11 are independently applied for the frequency bands.
In addition, when blockage is modeled according to Clause 7.6.4, the positions of blockers are the same across all the frequencies.
Note: The requirements above may not be fully aligned with the behavior of the model according to Clause 7.5, since cluster delays and angles will be frequency-dependent in scenarios where the DS or AS is frequency-dependent. The procedure below may alternatively be used to ensure that cluster delays and angles are frequency-independently generated.
7.6.5.1 Alternative channel generation method #
The alternative method replaces Steps 5-7 in Clause 7.5 with the below Steps 5'-7'. The inputs to the alternative method are the delay and angular spreads determined according to Step 4 at an anchor frequency, e.g. 2 GHz: DS0, ASD0, ASA0, ZSD0, ZSA0, the delay and angular spreads determined according to Step 4 at a frequency of interest: DS, ASD, ASA, ZSD, ZSA, and the number of clusters N from Table 7.5-6.
Step 5': Generate nominal delays 
and angles 
, 
, 
, 
.
Generate N delays from a single-sided exponential distribution with zero mean and standard deviation of 
, according to 
with 
.
Generate N AODs from a wrapped Gaussian distribution with zero mean and standard deviation of 
, according to 
with 
.
Generate N AOAs from a wrapped Gaussian distribution with zero mean and standard deviation of 
, according to 
with 
.
Generate N ZODs from a wrapped Laplacian distribution with zero mean and standard deviation of 
, according to 
with 
.
Generate N ZOAs from a wrapped Laplacian distribution with zero mean and standard deviation of 
, according to 
with 
.
r is a proportionality factor, r=1.5. The principal value of the arg function should be used, e.g. (-180,180).
In case of LOS, set 
, 
, 
, 
, and 
.
Step 6': Generate cluster powers 
.
Generate cluster powers as
(7.6.30a)
where 
is the per cluster shadowing term in [dB] and
(7.6.30b)
(7.6.30c)
(7.6.30d)
(7.6.30e)
(7.6.30f)
Normalize the cluster powers so that the sum of all cluster powers is equal to one, i.e.,
(7.6.30g)
or, in the case of LOS, so that
(7.6.30h)
where 
is the K-factor converted to linear scale.
Step 7': Generate delays 
and angles 
, 
, 
, 
.
(7.6.30i)
(7.6.30j)
(7.6.30k)
(7.6.30l)
(7.6.30m)
Repeat Steps 6'-7' for each frequency of interest, reusing the delays and angles from Step 5' for all frequencies.
Note: The resulting delay and angular spreads of channels generated with this alternative method will be similar but not identical to when using Steps 5-7 in Clause 7.5.
7.6 .6 Time-varying Doppler shift #
The Doppler shift generally depends on the time evolution of the channel as it is defined as the derivative of the channels phase over time. It can be the result from Tx, Rx, or scatterer movement. The more general form of the exponential Doppler term as used in Equation (7.5-22) is given by
. (7.6-31)
Here, 
is the normalized vector that points into the direction of the incoming wave as seen from the Rx at time 
. 
denotes the velocity vector of the Rx at time , while 
denotes a reference point in time that defines the initial phase, e.g. 
.
Note that Equation (7.5-22) only holds for time-invariant Doppler shift, i.e. 
.
7.6 .7 UT rotation #
UT rotation modelling is an add-on feature. When modelled, Step 1 in Clause 7.5 shall consider UT rotational motion.
Step 1:
Add: h) Give rotational motion of UT in terms of its bearing angle, downtilt angle and slant angle.
7.6 .8 Explicit ground reflection model #
In case the ground reflection shall be modelled explicitly, Equation (7.5-30) has to be replaced by
(7.6-32)
with the delays for the ground reflected and the LOS paths being defined by their lengths, according to the Tx-Rx separation 
and the Tx and Rx heights 
and 
respectively, as
, (7.6-33)
and
. (7.6-34)
The channel coefficient for the ground reflected path is given by
(7.6-35)
with the normalized vectors pointing towards the ground reflection point from the Tx
(7.6-36)
and from the Rx side
. (7.6-37)
The angles for the ground reflected path are given by the geometry assuming a flat surface with its normal pointing into z-direction. The angles at the Tx side can be determined by
(7.6-37a)
(7.6-37b)
and at the Rx side by
(7.6-37c)
. (7.6-37d)
The reflection coefficients for parallel and perpendicular polarization on the ground, cf. [21], are given by
, (7.6-38)
and
, (7.6-39)
with the complex relative permittivity of the ground material given by
. (7.6-40)
The electric constant 
is given by 8.854187817... × 10−12 F·m−1.
For applicable frequency ranges, the real relative permittivity can be modelled by
, (7.6-41)
while the conductivity in [S/m] may be modelled by
, (7.6-42)
with 
being the center frequency in Hz.
A selection of material properties from [19] is given below. Since the simplified models for conductivity and relative permittivity are only applicable for frequencies between 1 and 10 GHz, Figure 7.6.8-1 presents curves up to 100 GHz at least for very, medium dry and wet ground, cf. [20].
Table 7.6.8-1 Material properties [19]
Material class | Relative permittivity | Conductivity | Frequency range in [GHz] | ||
|
|
|
| ||
Concrete | 5.31 | 0 | 0.0326 | 0.8095 | 1-100 |
Brick | 3.75 | 0 | 0.038 | 0 | 1-10 |
Plasterboard | 2.94 | 0 | 0.0116 | 0.7076 | 1-100 |
Wood | 1.99 | 0 | 0.0047 | 1.0718 | 0.001-100 |
Floorboard | 3.66 | 0 | 0.0044 | 1.3515 | 50-100 |
Metal | 1 | 0 | 107 | 0 | 1-100 |
Very dry ground | 3 | 0 | 0.00015 | 2.52 | 1-10 |
Medium dry ground | 15 | −0.1 | 0.035 | 1.63 | 1-10 |
Wet ground | 30 | −0.4 | 0.15 | 1.30 | 1-10 |
Figure 7.6.8-1: Relative permittivity and conductivity as a function of frequency [20]
7.6.9 Absolute time of arrival #
To support simulations in which absolute time of arrival is important, the propagation time delay due to the total path length is considered in step 11 of the fast fading model as follows. The impulse response in NLOS is determined using equation (7.6-43) instead of (7.5-27) and the impulse response in LOS is determined using equation (7.6-44) instead of (7.5-30), where
where, in LOS case,
Table 7.6.9-1: Parameters for the absolute time of arrival model in NLOS
Scenarios | InH | InF-SL, InF-DL | InF-SH, InF-DH | UMi | UMa | RMa | SMa | |
-8.6 | -7.5 | -7.5 | -7.5 | -7.4 | -8.33 | -7.702 | ||
0.1 | 0.4 | 0.4 | 0.5 | 0.2 | 0.26 | 0.4 | ||
Correlation distance in the horizontal plane [m] | 10 | 6 | 11 | 15 | 50 | 50 | 50 | |
7.6.10 Dual mobility #
To support simulations that involve dual Tx and Rx mobility or scatterer mobility, the Doppler frequency component in the channel coefficient generation in step 11 in clause 7.5 should be updated as follows.
For the LOS path, the Doppler frequency is given by
where
For all other paths, the Doppler frequency component is given by:
where
7.6.11 Sources of EM interference #
When the simulation includes sources of EM interference in industrial scenarios, the channel(s) between the interferer(s) and the BS or UT should be modeled using the appropriate sub-scenario(s), e.g. clutter-embedded or elevated Tx or Rx and low or high clutter density. Characterization of EM interference sources is out of the scope of this TR.
7.6.12 Embedded devices #
To simulate devices that are embedded inside machinery or enclosures, an additional loss parameter Lembedded is added to the path loss in step 12. The additional path loss parameter Lembedded to embedded devices is left as a simulation assumption, where it may be formulated e.g. as a value or range, or derived using a methodology similar to the building penetration loss in clause 7.4.3.1.
7.6.13 Near-field channel model #
The near-field channel model is to support the simulations that involve the impacts of the spherical wavefront from the perspective of antenna array.
To model the antenna element-wise channel parameters, in the Step 11 in Clause 7.5, the following updates are considered to generate the channel impulse response:
For the NLOS channel impulse response, determine the NLOS channel coefficient for the two strongest clusters, say n = 1 and 2, instead of the equation (7.5-28) and for the N – 2 weakest clusters, say n = 3, 4,…, N, using
- To model the antenna element-wise phase at TRP side, the NLOS channel coefficient,
where, the
w here the
Table 7.6-13-1: Parameters for Uma, Umi, Indoor-Office and Indoor-Factory
Scenarios | UMa | UMi | InH | InF | |
k1 | 2 | 2 | 4 | 4 | |
Beta distribution | 1.93 | 1.53 | 1.25 | 1.38 | |
1.33 | 1.42 | 1.27 | 1.26 | ||
Optionally,
- To model the antenna element-wise phase at UT side additionally, the
where
w here
- To model the antenna element-wise antenna field patterns additionally, the
where
In the LOS channel impulse response, determine the LOS channel coefficient as:
- To model the antenna element-wise phase, the equation (7.5-29) in Clause 7.5 is replaced by:
where, the
Optionally,
- To model the element-wise antenna field patterns additionally, the equation (7.5-29) in Clause 7.5 is replaced by:
w here
7.6.14 Spatial non-stationarity channel model #
7.6.14.1 Spatial non-stationarity channel model at TRP side #
7.6.14.1.1 Introduction #
The spatial non-stationarity (SNS) channel model is to support the simulations that involve the impacts of the antenna element-wise power variation at TRP. Two alternative models (i.e., Physical blocker-based Model and Stochastic based Model) are provided for the SNS modelling.
From the perspective of the simulation requirements:
- The physical blocker-based model can be considered for simulation where physical accuracy and consistency are desired.
- The stochastic based model can be considered for simulation where computational efficient SNS modelling is desired.
From the perspective of the model mechanism:
- To reflect the SNS phenomenon due to partial blockage, the physical blocker-based model can be considered in simulation.
- To reflect the SNS phenomenon due to incomplete scattering, the stochastic based model can be considered in simulation.
The stochastic based model is recommended to be used in the simulation except for the case where it is not applicable.
7.6.14.1.2 Physical blocker-based Model #
To model the antenna element-wise power variation at TRP side for each UT, the following additional steps between Step 9 and 10 in Clause 7.5 are introduced to calculate the antenna element-wise power variation, i.e., for NLOS cluster,
Step I: Determine blockers
The number of blockers (
Note:
- Recommended parameters for typical blockers are provided in Table 7.6.14.1.1-1.
- The blocking effect diminishes with increasing distance to the blocker. For implementation purposes it may be sufficient to consider only the
Table 7.6.14.1.1-1: Blocker parameters
| Typical set of blockers | Blocker dimensions | Mobility pattern |
Indoor; Outdoor; InF | Human | Cartesian: w=0.3m; h=1.7m | Stationary or up to 3 km/h |
Outdoor | Vehicle | Cartesian: w=4.8m; h=1.4m | Stationary or up to 100 km/h |
InF | AGV | Cartesian: w=3m; h=1.5m | Up to 30 km/h |
InF | Industrial robot | Cartesian: w=2m; h=0.2m | Up to 3 m/s |
Outdoor | Billboard | Cartesian: w=2.4m; h=3.6m | Stationary |
Outdoor | Street lamp | Cartesian: w=0.4m; h=0.8m | Stationary |
Outdoor | Building edge | Cartesian: w=50 m; h=20 m | Stationary |
Indoor | Pillar | Cartesian: w=0.3m; h=3m | Stationary |
Step II: Determine the antenna element-wise power variation per ray for each element
The antenna element-wise power variation caused by each blocker to the mth ray of nth cluster for each element is modelled using a simple knife edge diffraction model and is given by:
where
-
- The side view plane is perpendicular to the horizontal ground plane. The top view is perpendicular to the side view.
- For each cluster and each element at BS side, the blocker screen is rotated around its centre such that the arrival (if the BS is receiver)/or departure (if the BS is transmitter) direction of the corresponding path is always perpendicular to the screen. It should be noted that different rotations are required for each individual ray and each antenna element. Meanwhile, the base and top edges of the screens are always parallel to the horizontal plane. As the screen is perpendicular to each ray,
Step III: Apply the antenna element-wise power variation in coefficient generation
The antenna element-wise power variation is considered in the Step 11 for coefficient generation in Clause 7.5 with following:
- For the NLOS cluster, the channel coefficients
- In the LOS case, the LOS channel coefficient
7.6.14.1.3 Stochastic based Model #
To model the antenna element-wise power variation at TRP side for each UT, the following additional steps between Step 9 and 10 in Clause 7.5 are introduced to calculate the antenna element-wise power attenuation factor, i.e.,
Step I: Determine the visibility limiting condition for the nth cluster of UT
For the nth cluster, the cluster is assumed to be impacted by the visibility limitation, if the
The
where
Table 7.6.14.1.2-1: Parameters for
| RMa | SMa | UMa | UMi | InH | InF |
0.14 | 0.24 | 0.56 | 0.49 | 0.31 | 0.32 | |
0.08 | 0.07 | 0.20 | 0.18 | 0.08 | 0.06 |
Step II: Generate the visibility region (VR)
For the nth cluster which is assumed to be impacted by the visibility limitation, the visibility region is generated as a rectangle region with dimension
where
where
Table 7.6.14.1.2-2: Parameters for A, B, R, and δ
Parameters | RMa | SMa | UMa | UMi | InH | InF | |
0.16 | 0.06 | 0.15 | 0.12 | 0 | 0 | ||
0.74 | 0.56 | 0.45 | 0.48 | 0.6 | 0.57 | ||
R | 60 | 23 | 33 | 50 | NA | NA | |
0 | 0 | 0 | 0 | 0 | 0 | ||
0.0016 | 0.0013 | 0.0015 | 0.001 | 0.0011 | 0.002 | ||
Each visibility region is assumed to be randomly located at one of the corners of TRP antenna array (i.e., reference conrner), which is determined by the following conditions:
- The probability of VR located at either left or right part of antenna array in horizontal domain is 0.5.
- The probability of VR located at either upper or lower part of antenna array in vertical domain is 0.5.
Step III: Determine the antenna element-wise power attenuation factor
For the nth cluster which is assumed not to be impacted by the visibility limitation, the
For the nth cluster which is assumed to be impacted by the visibility limitation, the
where,
Table 7.6.14.1.2-3: Parameters for C
| UMa/UMi/RMa/SMa/InF/InH |
13 |
Step IV: Apply the antenna element-wise power attenuation factor in coefficient generation
The antenna element-wise power variation is considered in the Step 11 for coefficient generation in Clause 7.5 with following:
- For the NLOS cluster, the channel coefficients
- In the LOS case, the LOS channel coefficient
7.6.14.2 Spatial non-stationarity channel model at UT side #
The spatial non-stationarity (SNS) channel model is to support the simulations that involve the impacts of the antenna element-wise power variation at UT.
The ratio of UT with impact of SNS is 90% and for each UT, the probability of each usage scenario is defined in Table 7.6.14.2-1:
Table 7.6.14.2-1: The probability of each scenario
Usage scenario | One hand grip | Dual hand grip | Head and one hand grip |
Probability | 58% | 29% | 13% |
The antenna element-wise power variation at UT side, i.e.,
Table 7.6.14.2-2: The power attenuation for each candidate antenna location
Antenna index | Power attenuation (dB) | ||||||||
One hand grip | Dual hand grip | Head and one hand grip | |||||||
Below 1 GHz | 1 - 8.4 GHz | 14.5 - 15.5 GHz
| Below 1 GHz | 1 - 8.4 GHz | 14.5 - 15.5 GHz
| Below 1 GHz | 1 - 8.4 GHz | 14.5 - 15.5 GHz | |
1 | - | 0.7 | 1.0 | - | 11.0 | 3.8 | - | 3.7 | 4.0 |
2 | - | 4.1 | 2.4 | - | 1.1 | 3.8 | - | 4.6 | 3.7 |
3 | - | 3.3 | 3.8 | - | 10.5 | 3.8 | - | 4.3 | 3.3 |
4 | 13.6 | 7.2 | 3.6 | 5.8 | 5.6 | 2.6 | 15.1 | 7.8 | 3.3 |
5 | - | 10.8 | 3.8 | - | 1.5 | 1.0 | - | 11.7 | 3.3 |
6 | - | 9.1 | 2.4 | - | 1.4 | 1.0 | - | 10.1 | 3.7 |
7 | - | 0.7 | 1.0 | - | 1.3 | 1.0 | - | 2.9 | 4.0 |
8 | 2.4 | 0.6 | 1.0 | 5.6 | 6.2 | 2.6 | 4.9 | 4.2 | 4.0 |
NOTE 1: For below 1GHz, the model is only applicable for 2-antenna system with antenna location index 4 and 8. NOTE 2: The selection of number of antenna and antenna index will be determined as part of the assumption for simulation. | |||||||||
The antenna element-wise power variation is applied in the Step 11 for coefficient generation in Clause 7.5.
7.6.15 Number of cluster variability model #
The number of clusters observed in real-world deployments can vary due to one or more factors, including but not limited to the propagation environment, carrier frequency, bandwidth, and spatial resolution. To capture this variability in simulations, the number of clusters for each link can be chosen between a closed range of [D1, D2]. The values of D1 and D2 are provided in Table 7.6.15-1. The selection of the number of clusters for each link is determined by the user of the model. The selected number of clusters for each link replaces the number of clusters N in Table 7.5.6-Part 1 and 7.5.6-Part 2. A similar framework for modeling the variability in number of clusters is also adopted in the METIS channel model [6].
Table 7.6.15-1: Range of number of clusters
Scenarios | LOS | NLOS | O2I |
UMi-Street Canyon | D1: 6 D2: 12 | D1: 6 D2: 19 | D1: 6 D2: 12 |
UMa | D1: 10 D2: 12 | D1: 15 D2: 20 | D1: 10 D2: 12 |
Indoor-office | D1: 7 D2: 15 | D1: 6 D2: 19 | N/A |
7.6.16 Polarization power variability model #
To support simulation of polarization power variability, the polarization variability powers
where
NOTE: Polarization power variability model is at least applicable for Model-2 of the antenna polarization modelling.
7. 7 Channel models for link-level evaluations #
7.7.1 Clustered Delay Line (CDL) models #
The CDL models are defined for the full frequency range from 0.5 GHz to 100 GHz with a maximum bandwidth of 2 GHz. CDL models can be implemented by e.g., coefficient generation Step 10 and Step 11 in Clause 7.5 or generating TDL model using spatial filter from Clause 7.7.4. Three CDL models, namely CDL-A, CDL-B and CDL-C are constructed to represent three different channel profiles for NLOS while CDL-D and CDL-E are constructed for LOS, the parameters of which can be found respectively in Table 7.7.1-1, Table 7.7.1-2 , Table 7.7.1-3, Table 7.7.1-4 and Table 7.7.1-5.
Each CDL model can be scaled in delay so that the model achieves a desired RMS delay spread, according to the procedure described in Clause 7.7.3. Each CDL model can also be scaled in angles so that the model achieves desired angle spreads, according to the procedure described in Clause 7.7.5.1.
For LOS channel models, the K-factor of CDL-D and CDL-E can be set to a desired value following the procedure described in Clause 7.7.6.
For modelling effect of beamforming in a simplified way, a brick-wall window can be applied to a delay-scaled CDL model. The power shall be normalized after applying the window. A TDL model for simplified evaluations can be obtained from the CDL model, according to this method.
The following step by step procedure should be used to generate channel coefficients using the CDL models.
Step 1: Generate departure and arrival angles
Generate arrival angles of azimuth using the following equation
, (7.7-0a)
Where φn,AOA is the cluster AOA and cASA is the cluster-wise rms azimuth spread of arrival angles (cluster ASA) in Tables 7.7.1.1 – 7.7.1.5 below, and αm denotes the ray offset angles within a cluster given by Table 7.5-3. If angular scaling according to Clause 7.7.5.1 is used, this is applied to the ray angles 
, The generation of AOD (
), ZSA (
), and ZSD (
) follows a procedure similar to AOA as described above.
Step 2: Coupling of rays within a cluster for both azimuth and elevation
Couple randomly AOD angles to AOA angles 
within a cluster n. Couple randomly ZOD angles 
with ZOA angles 
using the same procedure. Couple randomly AOD angles with ZOD angles 
within a cluster n.
Step 3: Generate the cross polarization power ratios
Generate the cross polarization power ratios (XPR) κfor each ray m of each cluster n as
, (7.7-0b)
where X is the per-cluster XPR in dB from Tables 7.7.1.1 – 7.7.1.5.
Step 4: Coefficient generation
Follow the same procedure as in Steps 10 and 11 in Clause 7.5, with the exception that all clusters are treated as "weaker cluster", i.e. no further sub-clusters in delay should be generated. Additional clusters representing delay spread of the stronger clusters are already provided in Tables 7.7.1.1 – 7.7.1.5.
Table 7.7.1-1. CDL-A
Cluster # | Normalized delay | Power in [dB] | AOD in [°] | AOA in [°] | ZOD in [°] | ZOA in [°] | |||||
1 | 0.0000 | -13.4 | -178.1 | 51.3 | 50.2 | 125.4 | |||||
2 | 0.3819 | 0 | -4.2 | -152.7 | 93.2 | 91.3 | |||||
3 | 0.4025 | -2.2 | -4.2 | -152.7 | 93.2 | 91.3 | |||||
4 | 0.5868 | -4 | -4.2 | -152.7 | 93.2 | 91.3 | |||||
5 | 0.4610 | -6 | 90.2 | 76.6 | 122 | 94 | |||||
6 | 0.5375 | -8.2 | 90.2 | 76.6 | 122 | 94 | |||||
7 | 0.6708 | -9.9 | 90.2 | 76.6 | 122 | 94 | |||||
8 | 0.5750 | -10.5 | 121.5 | -1.8 | 150.2 | 47.1 | |||||
9 | 0.7618 | -7.5 | -81.7 | -41.9 | 55.2 | 56 | |||||
10 | 1.5375 | -15.9 | 158.4 | 94.2 | 26.4 | 30.1 | |||||
11 | 1.8978 | -6.6 | -83 | 51.9 | 126.4 | 58.8 | |||||
12 | 2.2242 | -16.7 | 134.8 | -115.9 | 171.6 | 26 | |||||
13 | 2.1718 | -12.4 | -153 | 26.6 | 151.4 | 49.2 | |||||
14 | 2.4942 | -15.2 | -172 | 76.6 | 157.2 | 143.1 | |||||
15 | 2.5119 | -10.8 | -129.9 | -7 | 47.2 | 117.4 | |||||
16 | 3.0582 | -11.3 | -136 | -23 | 40.4 | 122.7 | |||||
17 | 4.0810 | -12.7 | 165.4 | -47.2 | 43.3 | 123.2 | |||||
18 | 4.4579 | -16.2 | 148.4 | 110.4 | 161.8 | 32.6 | |||||
19 | 4.5695 | -18.3 | 132.7 | 144.5 | 10.8 | 27.2 | |||||
20 | 4.7966 | -18.9 | -118.6 | 155.3 | 16.7 | 15.2 | |||||
21 | 5.0066 | -16.6 | -154.1 | 102 | 171.7 | 146 | |||||
22 | 5.3043 | -19.9 | 126.5 | -151.8 | 22.7 | 150.7 | |||||
23 | 9.6586 | -29.7 | -56.2 | 55.2 | 144.9 | 156.1 | |||||
Per-Cluster Parameters | |||||||||||
Parameter | cASD in [°] | cASA in [°] | cZSD in [°] | cZSA in [°] | XPR in [dB] | ||||||
Value | 5 | 11 | 3 | 3 | 10 | ||||||
Table 7.7.1-2. CDL-B
Cluster # | Normalized delay | Power in [dB] | AOD in [°] | AOA in [°] | ZOD in [°] | ZOA in [°] | |||||
1 | 0.0000 | 0 | 9.3 | -173.3 | 105.8 | 78.9 | |||||
2 | 0.1072 | -2.2 | 9.3 | -173.3 | 105.8 | 78.9 | |||||
3 | 0.2155 | -4 | 9.3 | -173.3 | 105.8 | 78.9 | |||||
4 | 0.2095 | -3.2 | -34.1 | 125.5 | 115.3 | 63.3 | |||||
5 | 0.2870 | -9.8 | -65.4 | -88.0 | 119.3 | 59.9 | |||||
6 | 0.2986 | -1.2 | -11.4 | 155.1 | 103.2 | 67.5 | |||||
7 | 0.3752 | -3.4 | -11.4 | 155.1 | 103.2 | 67.5 | |||||
8 | 0.5055 | -5.2 | -11.4 | 155.1 | 103.2 | 67.5 | |||||
9 | 0.3681 | -7.6 | -67.2 | -89.8 | 118.2 | 82.6 | |||||
10 | 0.3697 | -3 | 52.5 | 132.1 | 102.0 | 66.3 | |||||
11 | 0.5700 | -8.9 | -72 | -83.6 | 100.4 | 61.6 | |||||
12 | 0.5283 | -9 | 74.3 | 95.3 | 98.3 | 58.0 | |||||
13 | 1.1021 | -4.8 | -52.2 | 103.7 | 103.4 | 78.2 | |||||
14 | 1.2756 | -5.7 | -50.5 | -87.8 | 102.5 | 82.0 | |||||
15 | 1.5474 | -7.5 | 61.4 | -92.5 | 101.4 | 62.4 | |||||
16 | 1.7842 | -1.9 | 30.6 | -139.1 | 103.0 | 78.0 | |||||
17 | 2.0169 | -7.6 | -72.5 | -90.6 | 100.0 | 60.9 | |||||
18 | 2.8294 | -12.2 | -90.6 | 58.6 | 115.2 | 82.9 | |||||
19 | 3.0219 | -9.8 | -77.6 | -79.0 | 100.5 | 60.8 | |||||
20 | 3.6187 | -11.4 | -82.6 | 65.8 | 119.6 | 57.3 | |||||
21 | 4.1067 | -14.9 | -103.6 | 52.7 | 118.7 | 59.9 | |||||
22 | 4.2790 | -9.2 | 75.6 | 88.7 | 117.8 | 60.1 | |||||
23 | 4.7834 | -11.3 | -77.6 | -60.4 | 115.7 | 62.3 | |||||
Per-Cluster Parameters | |||||||||||
Parameter | cASD in [°] | cASA in [°] | cZSD in [°] | cZSA in [°] | XPR in [dB] | ||||||
Value | 10 | 22 | 3 | 7 | 8 | ||||||
Table 7.7.1-3. CDL-C
Cluster # | Normalized delay | Power in [dB] | AOD in [°] | AOA in [°] | ZOD in [°] | ZOA in [°] | |||||
1 | 0 | -4.4 | -46.6 | -101 | 97.2 | 87.6 | |||||
2 | 0.2099 | -1.2 | -22.8 | 120 | 98.6 | 72.1 | |||||
3 | 0.2219 | -3.5 | -22.8 | 120 | 98.6 | 72.1 | |||||
4 | 0.2329 | -5.2 | -22.8 | 120 | 98.6 | 72.1 | |||||
5 | 0.2176 | -2.5 | -40.7 | -127.5 | 100.6 | 70.1 | |||||
6 | 0.6366 | 0 | 0.3 | 170.4 | 99.2 | 75.3 | |||||
7 | 0.6448 | -2.2 | 0.3 | 170.4 | 99.2 | 75.3 | |||||
8 | 0.6560 | -3.9 | 0.3 | 170.4 | 99.2 | 75.3 | |||||
9 | 0.6584 | -7.4 | 73.1 | 55.4 | 105.2 | 67.4 | |||||
10 | 0.7935 | -7.1 | -64.5 | 66.5 | 95.3 | 63.8 | |||||
11 | 0.8213 | -10.7 | 80.2 | -48.1 | 106.1 | 71.4 | |||||
12 | 0.9336 | -11.1 | -97.1 | 46.9 | 93.5 | 60.5 | |||||
13 | 1.2285 | -5.1 | -55.3 | 68.1 | 103.7 | 90.6 | |||||
14 | 1.3083 | -6.8 | -64.3 | -68.7 | 104.2 | 60.1 | |||||
15 | 2.1704 | -8.7 | -78.5 | 81.5 | 93.0 | 61.0 | |||||
16 | 2.7105 | -13.2 | 102.7 | 30.7 | 104.2 | 100.7 | |||||
17 | 4.2589 | -13.9 | 99.2 | -16.4 | 94.9 | 62.3 | |||||
18 | 4.6003 | -13.9 | 88.8 | 3.8 | 93.1 | 66.7 | |||||
19 | 5.4902 | -15.8 | -101.9 | -13.7 | 92.2 | 52.9 | |||||
20 | 5.6077 | -17.1 | 92.2 | 9.7 | 106.7 | 61.8 | |||||
21 | 6.3065 | -16 | 93.3 | 5.6 | 93.0 | 51.9 | |||||
22 | 6.6374 | -15.7 | 106.6 | 0.7 | 92.9 | 61.7 | |||||
23 | 7.0427 | -21.6 | 119.5 | -21.9 | 105.2 | 58 | |||||
24 | 8.6523 | -22.8 | -123.8 | 33.6 | 107.8 | 57 | |||||
Per-Cluster Parameters | |||||||||||
Parameter | cASD in [°] | cASA in [°] | cZSD in [°] | cZSA in [°] | XPR in [dB] | ||||||
Value | 2 | 15 | 3 | 7 | 7 | ||||||
Table 7.7.1-4. CDL-D.
Cluster # | Cluster PAS | Normalized Delay | Power in [dB] | AOD in [°] | AOA in [°] | ZOD in [°] | ZOA in [°] | |
1 | Specular(LOS path) | 0 | -0.2 | 0 | -180 | 98.5 | 81.5 | |
Laplacian | 0 | -13.5 | 0 | -180 | 98.5 | 81.5 | ||
2 | Laplacian | 0.035 | -18.8 | 89.2 | 89.2 | 85.5 | 86.9 | |
3 | Laplacian | 0.612 | -21 | 89.2 | 89.2 | 85.5 | 86.9 | |
4 | Laplacian | 1.363 | -22.8 | 89.2 | 89.2 | 85.5 | 86.9 | |
5 | Laplacian | 1.405 | -17.9 | 13 | 163 | 97.5 | 79.4 | |
6 | Laplacian | 1.804 | -20.1 | 13 | 163 | 97.5 | 79.4 | |
7 | Laplacian | 2.596 | -21.9 | 13 | 163 | 97.5 | 79.4 | |
8 | Laplacian | 1.775 | -22.9 | 34.6 | -137 | 98.5 | 78.2 | |
9 | Laplacian | 4.042 | -27.8 | -64.5 | 74.5 | 88.4 | 73.6 | |
10 | Laplacian | 7.937 | -23.6 | -32.9 | 127.7 | 91.3 | 78.3 | |
11 | Laplacian | 9.424 | -24.8 | 52.6 | -119.6 | 103.8 | 87 | |
12 | Laplacian | 9.708 | -30.0 | -132.1 | -9.1 | 80.3 | 70.6 | |
13 | Laplacian | 12.525 | -27.7 | 77.2 | -83.8 | 86.5 | 72.9 | |
Per-Cluster Parameters | ||||||||
Parameter | cASD in [°] | cASA in [°] | cZSD in [°] | cZSA in [°] | XPR in [dB] | |||
Value | 5 | 8 | 3 | 3 | 11 | |||
Table 7.7.1-5. CDL-E.
Cluster # | Cluster PAS | Normalized Delay | Power in [dB] | AOD in [°] | AOA in [°] | ZOD in [°] | ZOA in [°] | ||
1 | Specular (LOS path) | 0.000 | -0.03 | 0 | -180 | 99.6 | 80.4 | ||
Laplacian | 0.000 | -22.03 | 0 | -180 | 99.6 | 80.4 | |||
2 | Laplacian | 0.5133 | -15.8 | 57.5 | 18.2 | 104.2 | 80.4 | ||
3 | Laplacian | 0.5440 | -18.1 | 57.5 | 18.2 | 104.2 | 80.4 | ||
4 | Laplacian | 0.5630 | -19.8 | 57.5 | 18.2 | 104.2 | 80.4 | ||
5 | Laplacian | 0.5440 | -22.9 | -20.1 | 101.8 | 99.4 | 80.8 | ||
6 | Laplacian | 0.7112 | -22.4 | 16.2 | 112.9 | 100.8 | 86.3 | ||
7 | Laplacian | 1.9092 | -18.6 | 9.3 | -155.5 | 98.8 | 82.7 | ||
8 | Laplacian | 1.9293 | -20.8 | 9.3 | -155.5 | 98.8 | 82.7 | ||
9 | Laplacian | 1.9589 | -22.6 | 9.3 | -155.5 | 98.8 | 82.7 | ||
10 | Laplacian | 2.6426 | -22.3 | 19 | -143.3 | 100.8 | 82.9 | ||
11 | Laplacian | 3.7136 | -25.6 | 32.7 | -94.7 | 96.4 | 88 | ||
12 | Laplacian | 5.4524 | -20.2 | 0.5 | 147 | 98.9 | 81 | ||
13 | Laplacian | 12.0034 | -29.8 | 55.9 | -36.2 | 95.6 | 88.6 | ||
14 | Laplacian | 20.6419 | -29.2 | 57.6 | -26 | 104.6 | 78.3 | ||
Per-Cluster Parameters | |||||||||
Parameter | cASD in [°] | cASA in [°] | cZSD in [°] | cZSA in [°] | XPR in [dB] | ||||
Value | 5 | 11 | 3 | 7 | 8 | ||||
7. 7.2 Tapped Delay Line (TDL) models #
The TDL models for simplified evaluations, e.g., for non-MIMO evaluations, are defined for the full frequency range from 0.5 GHz to 100 GHz with a maximum bandwidth of 2 GHz.
Three TDL models, namely TDL-A, TDL-B and TDL-C, are constructed to represent three different channel profiles for NLOS while TDL-D and TDL-E are constructed for LOS, the parameters of which can be found respectively in Table 7.7.2-1, Table 7.7.2-2 , Table 7.7.2-3, Table 7.7.2-4 and Table 7.7.2-5.
The Doppler spectrum for each tap is characterized by a classical (Jakes) spectrum shape and a maximum Doppler shift fD where 
. Due to the presence of a LOS path, the first tap in TDL-D and TDL-E follows a Ricean fading distribution. For those taps the Doppler spectrum additionally contains a peak at the Doppler shift fS = 0.7 fD with an amplitude such that the resulting fading distribution has the specified K-factor.
Each TDL model can be scaled in delay so that the model achieves a desired RMS delay spread, according to the procedure described in Clause 7.7.3.
For LOS channel models, the K-factor of TDL-D and TDL-E can be set to a desired value following the procedure described in Clause 7.7.6.
Table 7.7.2-1. TDL-A
Tap # | Normalized delay | Power in [dB] | Fading distribution |
1 | 0.0000 | -13.4 | Rayleigh |
2 | 0.3819 | 0 | Rayleigh |
3 | 0.4025 | -2.2 | Rayleigh |
4 | 0.5868 | -4 | Rayleigh |
5 | 0.4610 | -6 | Rayleigh |
6 | 0.5375 | -8.2 | Rayleigh |
7 | 0.6708 | -9.9 | Rayleigh |
8 | 0.5750 | -10.5 | Rayleigh |
9 | 0.7618 | -7.5 | Rayleigh |
10 | 1.5375 | -15.9 | Rayleigh |
11 | 1.8978 | -6.6 | Rayleigh |
12 | 2.2242 | -16.7 | Rayleigh |
13 | 2.1718 | -12.4 | Rayleigh |
14 | 2.4942 | -15.2 | Rayleigh |
15 | 2.5119 | -10.8 | Rayleigh |
16 | 3.0582 | -11.3 | Rayleigh |
17 | 4.0810 | -12.7 | Rayleigh |
18 | 4.4579 | -16.2 | Rayleigh |
19 | 4.5695 | -18.3 | Rayleigh |
20 | 4.7966 | -18.9 | Rayleigh |
21 | 5.0066 | -16.6 | Rayleigh |
22 | 5.3043 | -19.9 | Rayleigh |
23 | 9.6586 | -29.7 | Rayleigh |
Table 7.7.2-2. TDL-B
Tap # | Normalized delay | Power in [dB] | Fading distribution |
1 | 0.0000 | 0 | Rayleigh |
2 | 0.1072 | -2.2 | Rayleigh |
3 | 0.2155 | -4 | Rayleigh |
4 | 0.2095 | -3.2 | Rayleigh |
5 | 0.2870 | -9.8 | Rayleigh |
6 | 0.2986 | -1.2 | Rayleigh |
7 | 0.3752 | -3.4 | Rayleigh |
8 | 0.5055 | -5.2 | Rayleigh |
9 | 0.3681 | -7.6 | Rayleigh |
10 | 0.3697 | -3 | Rayleigh |
11 | 0.5700 | -8.9 | Rayleigh |
12 | 0.5283 | -9 | Rayleigh |
13 | 1.1021 | -4.8 | Rayleigh |
14 | 1.2756 | -5.7 | Rayleigh |
15 | 1.5474 | -7.5 | Rayleigh |
16 | 1.7842 | -1.9 | Rayleigh |
17 | 2.0169 | -7.6 | Rayleigh |
18 | 2.8294 | -12.2 | Rayleigh |
19 | 3.0219 | -9.8 | Rayleigh |
20 | 3.6187 | -11.4 | Rayleigh |
21 | 4.1067 | -14.9 | Rayleigh |
22 | 4.2790 | -9.2 | Rayleigh |
23 | 4.7834 | -11.3 | Rayleigh |
Table 7.7.2-3. TDL-C
Tap # | Normalized delays | Power in [dB] | Fading distribution |
1 | 0 | -4.4 | Rayleigh |
2 | 0.2099 | -1.2 | Rayleigh |
3 | 0.2219 | -3.5 | Rayleigh |
4 | 0.2329 | -5.2 | Rayleigh |
5 | 0.2176 | -2.5 | Rayleigh |
6 | 0.6366 | 0 | Rayleigh |
7 | 0.6448 | -2.2 | Rayleigh |
8 | 0.6560 | -3.9 | Rayleigh |
9 | 0.6584 | -7.4 | Rayleigh |
10 | 0.7935 | -7.1 | Rayleigh |
11 | 0.8213 | -10.7 | Rayleigh |
12 | 0.9336 | -11.1 | Rayleigh |
13 | 1.2285 | -5.1 | Rayleigh |
14 | 1.3083 | -6.8 | Rayleigh |
15 | 2.1704 | -8.7 | Rayleigh |
16 | 2.7105 | -13.2 | Rayleigh |
17 | 4.2589 | -13.9 | Rayleigh |
18 | 4.6003 | -13.9 | Rayleigh |
19 | 5.4902 | -15.8 | Rayleigh |
20 | 5.6077 | -17.1 | Rayleigh |
21 | 6.3065 | -16 | Rayleigh |
22 | 6.6374 | -15.7 | Rayleigh |
23 | 7.0427 | -21.6 | Rayleigh |
24 | 8.6523 | -22.8 | Rayleigh |
Table 7.7.2-4. TDL-D.
Tap # | Normalized delay | Power in [dB] | Fading distribution |
1 | 0 | -0.2 | LOS path |
0 | -13.5 | Rayleigh | |
2 | 0.035 | -18.8 | Rayleigh |
3 | 0.612 | -21 | Rayleigh |
4 | 1.363 | -22.8 | Rayleigh |
5 | 1.405 | -17.9 | Rayleigh |
6 | 1.804 | -20.1 | Rayleigh |
7 | 2.596 | -21.9 | Rayleigh |
8 | 1.775 | -22.9 | Rayleigh |
9 | 4.042 | -27.8 | Rayleigh |
10 | 7.937 | -23.6 | Rayleigh |
11 | 9.424 | -24.8 | Rayleigh |
12 | 9.708 | -30.0 | Rayleigh |
13 | 12.525 | -27.7 | Rayleigh |
NOTE: The first tap follows a Ricean distribution with a K-factor of K1 = 13.3 dB and a mean power of 0dB. | |||
Table 7.7.2-5. TDL-E.
Tap # | Normalized delay | Power in [dB] | Fading distribution |
1 | 0 | -0.03 | LOS path |
0 | -22.03 | Rayleigh | |
2 | 0.5133 | -15.8 | Rayleigh |
3 | 0.5440 | -18.1 | Rayleigh |
4 | 0.5630 | -19.8 | Rayleigh |
5 | 0.5440 | -22.9 | Rayleigh |
6 | 0.7112 | -22.4 | Rayleigh |
7 | 1.9092 | -18.6 | Rayleigh |
8 | 1.9293 | -20.8 | Rayleigh |
9 | 1.9589 | -22.6 | Rayleigh |
10 | 2.6426 | -22.3 | Rayleigh |
11 | 3.7136 | -25.6 | Rayleigh |
12 | 5.4524 | -20.2 | Rayleigh |
13 | 12.0034 | -29.8 | Rayleigh |
14 | 20.6519 | -29.2 | Rayleigh |
NOTE: The first tap follows a Ricean distribution with a K-factor of K1 = 22 dB and a mean power of 0dB. | |||
7. 7.3 Scaling of delays #
The RMS delay spread values of both CDL and TDL models are normalized and they can be scaled in delay so that a desired RMS delay spread can be achieved. The scaled delays can be obtained according to the following equation:
(7.7-1)
in which
is the normalized delay value of the nth cluster in a CDL or a TDL model
is the new delay value (in [ns]) of the nth cluster
is the wanted delay spread (in [ns])
The example scaling parameters are selected according to Table 7.7.3-1 where the values have been chosen such that the RMS delay spreads span the range observed in measurements corresponding to the typical 5G evaluation scenarios. For information purposes, examples of such RMS delay spreads for the different scenarios are given in Table 7.7.3-2 where the "short-delay profile" corresponds to the median RMS delay spread for LOS scenarios, while "normal-delay profile" and "long-delay profile" correspond to the median and the 90th percentile RMS delay spread for NLOS scenarios according to the channel parameters in Table 7.5-6. It can therefore be understood that a particular RMS delay spread in Table 7.7.3-1 may occur in any scenario; however certain values may be more likely in some scenarios than in others.
The example parameters given in Table 7.7.3-1 does not preclude the use of other scaling values if this is found appropriate, for instance if additional scenarios are introduced or if e.g. the effect of beamforming needs to be captured in a TDL. Both of these examples can potentially result in an increased range of experienced RMS delay spreads.
Table 7.7.3-1. Example scaling parameters for CDL and TDL models.
Model |
|
Very short delay spread | 10 ns |
Short delay spread | 30 ns |
Nominal delay spread | 100 ns |
Long delay spread | 300 ns |
Very long delay spread | 1000 ns |
Table 7.7.3-2. Scenario specific scaling factors - for information only
Proposed Scaling Factor | Frequency [GHz] | |||||||
2 | 6 | 15 | 28 | 39 | 60 | 70 | ||
Indoor office | Short-delay profile | 20 | 16 | 16 | 16 | 16 | 16 | 16 |
Normal-delay profile | 39 | 30 | 24 | 20 | 18 | 16 | 16 | |
Long-delay profile | 59 | 53 | 47 | 43 | 41 | 38 | 37 | |
UMi Street-canyon | Short-delay profile | 65 | 45 | 37 | 32 | 30 | 27 | 26 |
Normal-delay profile | 129 | 93 | 76 | 66 | 61 | 55 | 53 | |
Long-delay profile | 634 | 316 | 307 | 301 | 297 | 293 | 291 | |
UMa | Short-delay profile | 93 | 93 | 85 | 80 | 78 | 75 | 74 |
Normal-delay profile | 363 | 363 | 302 | 266 | 249 | 228 | 221 | |
Long-delay profile | 1148 | 1148 | 955 | 841 | 786 | 720 | 698 | |
RMa & RMa O2I | Short-delay profile | 32 | 32 | N/A | N/A | N/A | N/A | N/A |
Normal-delay profile | 37 | 37 | N/A | N/A | N/A | N/A | N/A | |
Long-delay profile | 153 | 153 | N/A | N/A | N/A | N/A | N/A | |
UMi / UMa O2I | Normal-delay profile | 240 | ||||||
Long-delay profile | 616 | |||||||
7.7.4 Spatial filter for generating TDL channel model #
The TDL models described in Clause 7.7.2 are generated from the CDL models assuming ideal isotropic antennas at both Tx and Rx. It is also possible to generate TDL models by assuming non-isotropic antennas like directive horn antennas or array antennas.
The basic idea to generate a TDL model based on a filtered CDL model is shown in Figure 7.7.4-1 below.
Figure 7.7.4-1 The basic idea for filtering the CDL model to TDL model.
7.7.4.1 Exemplary filters/antenna patterns #
Note that any filter/pattern can be applied on a CDL to derive a TDL for evaluating directional algorithms.
Example 1: Isotropic pattern
(7.7-2)
Example 2: Rectangular mask
(7.7-3)
with 
denotes beamwidth.
Example 3: Simplified antenna pattern given in [ITU-R M.2135]
Figure 7.7.4-2 Simplified antenna pattern [ITU-R M.2135].
7.7.4.2 Generation procedure #
1. The following steps are needed to generate tapped delay line (TDL) models:Choose a CDL model (e.g. CDL-A). Note that the models may be scaled according to Clause 7.7.5.1 prior to the filtering in order to represent different angular spreads.
2. Choose spatial filters 
and 
defined in LCS
3. Transform the spatial filter into GCS to obtain 
and 
such that the pointing direction 
is centered within the filter. The pointing direction may be defined
a. by the dominant path 
with 
, where 
denotes the CDL cluster power values
b. Or an arbitrary direction
4. Calculate TDL cluster power values 
given the following equation
(7.7-4)
7. 7.5 Extension for MIMO simulations #
Extended MIMO link-level channel models can be constructed according to two alternative methods described in the following.
7. 7.5.1 CDL extension: Scaling of angles #
The angle values of CDL models are fixed, which is not very suitable for MIMO simulations for several reasons; The PMI statistics can become biased, and a fixed precoder may perform better than open-loop and on par with closed-loop or reciprocity beamforming. Furthermore, a CDL only represents a single channel realization. The predefined angle values in the CDL models can be generalized by introducing angular translation and scaling. By translation, mean angle
in which:
-
-
- s is a scale factor chosen to change the distribution of the angles based on calculation in Annex A.5. Table 7.7.5.1-1 shows required scale factor for typical desired angular spread values for AOD, AOD, ZOA, and ZOD,
-
-
-
-
-
-
-
-
-
Table 7.7.5.1-1: Scale factor values for each CDL model
CDL Type | Desired AOD Spread（°） | Scale Factor (AOD) | Desired AOA Spread（°） | Scale Factor (AOA) | Desired ZOA Spread（°） | Scale Factor (ZOA) | Desired ZOD Spread（°） | Scale Factor (ZOD) |
CDL-A | 5 | 0.0680 | 30 | 0.3531 | 5 | 0.2397 | 1 | 0.0352 |
10 | 0.1360 | 45 | 0.5268 | 10 | 0.4802 | 3 | 0.1056 | |
15 | 0.2041 | 60 | 0.6981 | 15 | 0.7225 | 5 | 0.1761 | |
25 | 0.3405 |
|
|
|
|
|
| |
CDL-B | 5 | 0.1238 | 30 | 0.5417 | 5 | 0.6519 | 1 | 0.1940 |
10 | 0.2475 | 45 | 0.8081 | 10 | 1.3018 | 3 | 0.5822 | |
15 | 0.3710 | 60 | 1.0709 | 15 | 1.9480 | 5 | 0.9705 | |
25 | 0.6168 |
|
|
|
|
|
| |
CDL-C | 5 | 0.1281 | 30 | 0.4307 | 5 | 0.6476 | 1 | 0.3643 |
10 | 0.2568 | 45 | 0.6447 | 10 | 1.2971 | 3 | 1.0929 | |
15 | 0.3864 | 60 | 0.8585 | 15 | 1.9504 | 5 | 1.8219 | |
25 | 0.6513 |
|
|
|
|
|
| |
CDL-D | 5 | 0.3231 | 30 | 9.8888 | 5 | 4.3268 | 1 | 0.4477 |
10 | 0.6652 | 45 | N/A | 10 | 8.8868 | 3 | 1.3469 | |
15 | 1.0594 | 60 | N/A | 15 | 14.0344 | 5 | 2.2579 | |
25 | 5.8637 |
|
|
|
|
|
| |
CDL-E | 5 | 0.3950 | 30 | 2.9733 | 5 | 6.9195 | 1 | 0.9714 |
10 | 0.8009 | 45 | N/A | 10 | 14.8378 | 3 | 2.9180 | |
15 | 1.2330 | 60 | N/A | 15 | 27.2849 | 5 | 4.8774 | |
25 | 2.3627 |
|
|
| 0.2397 |
|
| |
NOTE: Values of Table 7.7.5.1-1 were computed based on scaling factor calculation method described in Annex A.5. | ||||||||
Alternatively, for CDL-D and CDL-E, if maintaining the LOS angle is desired, the translated and scaled ray angles can be obtained according to the following equation instead of using equation (7.7-5) and (7.7-5a):
in which:
-
-
The angular scaling is applied on the cluster angles including offsets from the tabulated cluster angles. Typical angular spreads for different scenarios can be obtained from the system-level model.
Example scaling values are:
- AOD spread (ASD) for each CDL model: {5, 10, 15, 25} degrees.
- AOA spread (ASA) for each CDL model: {30, 45, 60} degrees.
- ZOA spread (ZSA) for each CDL model: {5, 10, 15} degrees.
- ZOD spread (ZSD) for each CDL model: {1, 3, 5} degrees.
The angular scaling and translation can be applied to some or all of the azimuth and zenith angles of departure and arrival.
Note: The azimuth angles may need to be wrapped around to be within [0, 360] degrees, while the zenith angles may need to be clipped to be within [0, 180] degrees.
7. 7.5.2 TDL extension: Applying a correlation matrix #
The TDLs and the spatial-filtered TDLs can be used with the correlation matrices for MIMO link-level simulations. Typical correlation parameters can be derived from 1) delay & angular scaled CDLs with antenna array assumptions, 2) system-level model with antenna array assumptions, or 3) by selecting extreme cases, e.g. uncorrelated, highly correlated etc. For example, these following options can be considered:
1) Zero correlation (IID channel coefficients) can be used for any number of antenna elements
2) The correlation matrix construction method from TS36.101/104 [15][16] can be used for linear and planar (single- or dual-polarized) arrays.
- Other correlation parameters α, β, γ than those specified in TS36.101/104 [15][16] and extensions to larger antenna arrays can be considered. For typical scenarios, α and β will be in the range 0-1
- A representative set of values is {0,0.7,0.9,0.99}
Note: This approach can be applied to TDLs derived from spatially filtered CDLs to emulate hybrid BF system
Note: Other methodologies could also be developed, e.g.
- extending the TS36.101/104 [15][16] procedure to planar arrays or more elements
- using CDLs in combination with array assumptions to derive per-tap correlation matrices as in [17].
- using the system-level model in combination with array assumptions to derive per-tap or per-channel correlation matrices
7. 7.6 K-factor for LOS channel models #
For the LOS channel models of CDL/TDL-D and CDL/TDL-E, the K-factor values may be changed by the user. Mean and standard deviation of K-factor values can be found in Table 7.5-6, although other values may also be used.
If the K-factor of a model shall be changed to 
[dB], the cluster powers for the Laplacian clusters (in case of CDL) or the tap powers for the Rayleigh fading taps (in case of TDL) are determined by
, (7.7.6-1)
where 
and 
denote the scaled and the model path power (as given in the tables) of tap/cluster 
. The model's K-factor 
is defined as
. (7.7.6-2)
After scaling the powers, the delay spread needs to be re-normalized. This is done through the two steps below.
1) Calculate the actual RMS delay spread after the K-factor adjustment.
2) Divide the delays by that value to obtain DS = 1.
7. 8 Channel model calibration #
7. 8.1 Large scale calibration #
For large scale calibration, fast fading is not modeled. The calibration parameters can be found in Table 7.8-1. The calibration results based on TR 38.900 V14.0.0 can be found in R1-165974.
Table 7.8-1: Simulation assumptions for large scale calibration
Parameter | Values |
Scenarios | UMa, UMi-Street Canyon, Indoor-office (open office) |
Sectorization | 3 sectors per cell site: 30, 150 and 270 degrees
|
BS antenna configurations | Mg = Ng = 1; (M,N,P) = (10, 1, 1), dV = 0.5λ |
BS port mapping | The 10 elements are mapped to a single CRS port |
BS antenna electrical downtilting | 102 degrees for UMa and UMi Street Canyon 110 degrees for indoor |
Antenna virtualization | DFT precoding according to TR 36.897 with application of panning and tilting angles |
BS Tx power | 44 dBm for UMi-Street Canyon, 49 for UMa at 6GHz 35 dBm at 30GHz and 70 GHz for UMa and UMi-Street canyon 24 dBm for Indoor for all carrier frequencies |
Bandwidth | 20MHz for 6GHz, and 100MHz for 30GHz and 70 GHz |
UT antenna configurations | 1 element (vertically polarized), Isotropic antenna gain pattern |
Handover margin (for calibration) | 0dB |
UT distribution | Following TR36.873 for UMa and UMi, (3D dropping) uniform dropping for indoor with minimum distance (2D) of 0 m |
UT attachment | Based on pathloss considering LOS angle |
UT noise figure | 9 dB |
Fast fading channel | Fast fading channel is not modelled |
O2I penetration loss | 50% low loss and 50% high loss |
Carrier Frequency | 6 GHz, 30 GHz, 70GHz |
Wrapping method for UMa and UMi | geographical distance based wrapping (mandatory) radio distance (optional) |
Metrics | 1) Coupling loss – serving cell (based on LOS pathloss) |
2) Geometry (based on LOS pathloss) with and without white noise |
Additional calibration parameters can be found in Table 7.8-1A. It is assumed that parameters from Table 7.8-1 is used if unspecified by the additional calibration parameters in Table 7.8-1A. In addition, calibration of UMa and UMi-Street Canyon at 6 GHz carrier frequency using simulation assumptions in Table 7.8-1 with updated channel modeling is part of the additional calibration. The calibration results based on additional calibration parameters can be found in R1-2506406.
Table 7.8-1A: Simulation assumptions for large scale calibration
Parameter | Values |
Scenarios | UMa, UMi-Street Canyon, SMa |
Carrier Frequency | 7 GHz |
BS antenna downtilting | Mechanical downtilt of 95 degrees for SMa for ISD = 1299m Mechanical downtilt of 92 degrees for SMa for ISD = 1732m Electrical downtilt in Table 7.8-1 for UMa and UMi-Street Canyon |
BS antenna configurations | Mg = Ng = 1; (M,N,P) = (10, 1, 1), dV = 0.5λ |
BS port mapping | Mp = 1, Np = 1, i.e., 10 elements are mapped to a single port Mp and Np are the number of vertical, horizontal TXRUs within a panel and polarization |
BS Tx power | 49 dBm for SMa |
Bandwidth | 20 MHz |
UT distribution | For SMa, 20% of UT outdoor, 80% of UT indoor. Among indoor UT, 90% of indoor UT are within residential buildings, and 10% of indoor UT in commercial buildings. Indoor UTs are uniformly distributed across all floors for a building type. |
LOS probability | For SMa, 0% vegetation |
O2I penetration loss | For SMa, Low-loss A model |
O2I car penetration loss | For SMa, non-metallic car window |
Wrapping method | For UMa, UMi-Street Canyon, SMa: - geographical distance based wrapping (mandatory) - radio distance (optional) |
7. 8.2 Full calibration #
The calibration parameters for full calibration including the fast fading modelling can be found in Table 7.8-2. Unspecified parameters in Table 7.8-2 are the same as those in Table 7.8-1. When P=2, X-pol (+/-45 degree) is used for BS antenna configuration 1 and X-pol (0/+90 degree) is used for UT antenna configuration. The calibration results based on TR 38.900 V14.0.0 can be found in R1-165975.
Table 7.8-2: Simulation assumptions for full calibration
Parameter | Values |
Scenarios | UMa, UMi-street Canyon, Indoor-office (open office) |
Carrier Frequency | 6 GHz, 30 GHz, 60GHz, 70GHz |
Bandwidth | 20MHz for 6GHz, and 100MHz for 30GHz, 60 GHz and 70 GHz |
BS Tx power | 44 dBm for UMi-Street Canyon, 49 for UMa at 6GHz 35 dBm at 30GHz, 60 GHz and 70 GHz for UMa and UMi-Street canyon 24 dBm for Indoor for all carrier frequencies |
BS antenna configurations | Config 1: M = 4,N = 4,P = 2, Mg = 1, Ng = 2, dH = dV = 0.5λ, dH,g = dV,g = 2.5λ … calibration metrics 1), 2), 3) are calibrated Config 2: Mg = Ng = 1, M = N = 2, P = 1 … calibration metrics 1), 2), 4) are calibrated |
BS port mapping | Config 1: all 16 elements for each polarization on each panel are mapped to a single CRS port; panning angles of the two subarrays: (0,0) degs; same downtilt angles as used for the large-scale calibrations Config 2: each antenna element is mapped to one CRS port |
UT antenna configurations | Mg = Ng = 1, M = N = 1, P = 2
|
UT distribution | Following TR36.873 for UMa and UMi, (3D dropping) uniform dropping for indoor with minimum distance (2D) of 0 m |
UT attachment | Based on RSRP (formula) from CRS port 0 |
Polarized antenna modelling | Model-2 in TR36.873 |
UT orientation | ΩUT,α uniformly distributed on [0,360] degree, ΩUT,β= 90 degree, ΩUT,γ = 0 degree |
UT antenna pattern | Isotropic |
Metrics | 1) Coupling loss – serving cell |
2) Wideband SIR before receiver without noise | |
3) CDF of Delay Spread and Angle Spread (ASD, ZSD, ASA, ZSA) from the serving cell (according to circular angle spread definition of TR 25.996) | |
4) CDF of largest (1st) PRB singular values (serving cell) at t=0 plotted in 10*log10 scale. CDF of smallest (2nd) PRB singular values (serving cell) at t=0 plotted in 10*log10 scale. CDF of the ratio between the largest PRB singular value and the smallest PRB singular value (serving cell) at t=0 plotted in 10*log10 scale. See note. | |
NOTE: The PRB singular values of a PRB are the eigenvalues of the mean covariance matrix in the PRB. | |
Additional full calibration parameters can be found in Table 7.8-2A. It is assumed that parameters from Table 7.8-2 is used if unspecified by the additional full calibration parameters in Table 7.8-2A. In addition, calibration of UMa and UMi-Street Canyon at 6 GHz carrier frequency using simulation assumptions in Table 7.8-2 with updated channel modeling is part of the additional calibration. For calibration of UMa and UMi-Street Canyon at 6 GHz, the following is additionally assumed.
- SCS of 15 kHz
- UT attachment is based on RSRP (formula) from BS port 0
- BS antenna configuration 1 and 2 both apply
- UT antenna configuration, pattern, and polarization modeling are labeled as UT antenna config A
The calibration results based on additional calibration parameters can be found in R1-2506406.
Table 7.8-2A: Simulation assumptions for full calibration
Parameter | Values |
Scenarios | UMa, UMi-Street Canyon, SMa |
Carrier Frequency | 7 GHz, (optional) 15 GHz |
BS antenna downtilting | Mechanical downtilt of 95 degrees for SMa for ISD = 1299m Mechanical downtilt of 92 degrees for SMa for ISD = 1732m Electrical downtilt as in Table 7.8-1 for UMa and UMi-Street Canyon |
BS antenna configurations | Config 3 for UMi, UMa, SMa at 7 GHz: Mg = Ng = 1, M = 8, N = 16, P = 2, dH = dV = 0.5λ … calibration metrics 1), 2), 3), 4) are calibrated (optional) Config 4 for UMa at 7 and 15 GHz: Mg = Ng = 1, M = 64, N = 16, P = 2, Mg = 1, Ng = 1, dH = dV = 0.5λ … calibration metrics 1), 2), 3), 4) are calibrated |
BS Polarized antenna modelling | Model-2 in Clause 7.3.2 |
BS port mapping | Config 3 for UMi, UMa, SMa at 7 GHz: Mp = 8, Np = 16, each antenna element is mapped to one port (optional) Config 4 for UMa at 7 and 15 GHz: Mp = 16, Np = 16 Mp and Np are the number of vertical, horizontal TXRUs within a panel and polarization |
BS Tx power | 49 dBm for SMa |
Bandwidth | 20 MHz for 7 GHz (optional) 200 MHz for 7 and 15 GHz |
UT attachment | Based on RSRP (formula) from BS port 0 |
UT distribution | For SMa, 20% of UT outdoor, 80% of UT indoor. Among indoor UT, 90% of indoor UT are within residential buildings, and 10% of indoor UT in commercial buildings. Indoor UTs are uniformly distributed across all floors for a building type. |
UT array orientation | Config B, C: ΩUT,α uniformly distributed on [0,360] degree, ΩUT,β= 45 degree, ΩUT,γ = 0 degree Config D: ΩUT,α = 0 degree, ΩUT,β= 0 degree, ΩUT,γ = 0 degree |
UT antenna configurations | Config B for 7 GHz: 4 antenna port with single polarization for calibration based on handheld device antenna model using candidate antenna locations (1,7,3,5) as described in Clause 7.3 (optional) Config C for 15 GHz: 16 antenna port with dual polarization based on handheld device antenna model using candidate antenna locations in (1,2,3,4,5,6,7,8) as described in Clause 7.3 (only for metric 5) Config D: 8 antenna port with single polarization based on handheld device antenna model using candidate antenna locations in (1,2,3,4,5,6,7,8) as described in Clause 7.3 |
UT antenna pattern | Config B, C, D: Based on directional antenna for handheld UT described in Clause 7.3 |
UT Polarized antenna modelling | Config B, C, D: Based on directional antenna for handheld UT described in Clause 7.3 |
O2I penetration loss | For SMa, low-loss A model |
SCS assumption | 30 kHz |
Additional metrics | 5) Antenna field pattern of handheld UT, |
7. 8.3 Calibration of additional features #
The calibration parameters for the calibration of oxygen absorption, large bandwidth and large antenna array, spatial consistency, and blockage can be respectively found in Table 7.8-3, 7.8-4, 7.8-5, and 7.8-6. Unspecified parameters in these tables are the same as those in Tables 7.8-1 and 7.8-2. When P=2, X-pol (+/-45 degree) is used for BS antenna configuration 1 and X-pol (0/+90 degree) is used for UT antenna configuration. The calibration results based on TR 38.900 V14.0.0 can be found in R1-1700990.
The additional calibration parameters for the calibration of near field channel modeling, BS side spatial non-stationarity, and UT side spatial non-stationarity can be respectively found in Table 7.8-6A, 7.8-6B, and 7.8-6C. For the assumption on the aperture size of the antenna array, the following guidance is considered:
- Up to 1.5 m for UMa with maximum antenna elements in the array is 5k for single Polarization.
- Up to 1 m for UMi with maximum antenna elements in the array is 2.22k for single Polarization.
- Up to 0.71 m for Indoor factory with maximum antenna elements in the array is 1.12k for single Polarization.
- Up to 0.25 (for rectangular antenna array), 0.5 (for linear antenna array) m for Indoor office with maximum antenna elements in the array is 256, 80 for single Polarization, respectively.
The additional calibration results can be found in R1-2506406.
Table 7.8-3: Simulation assumptions for calibration for oxygen absorption
Parameter | Values |
Scenarios | UMi-street Canyon |
Carrier Frequency | 60 GHz |
BS antenna configurations | M = 4, N = 4, P = 2, Mg = 1, Ng = 2, dH = dV = 0.5λ, dH,g = dV,g = 2.5λ |
BS port mapping | all 16 elements for each polarization on each panel are mapped to a single CRS port; panning angles of the two subarrays: (0,0) degs; same downtilt angles as used for the large-scale calibrations |
Calibration method | Drop multiple users in the multiple cells randomly, and collect the following metrics for each user after attachment. |
Metrics | 1) CDF of coupling loss (serving cell) 2) Wideband SINR before receiver – determined from RSRP (formula) from CRS port 0 3) CDF of Delay Spread from the serving cell |
Table 7.8-4: Simulation assumptions for calibration for large bandwidth and large antenna array
Parameter | Values |
Scenarios | UMi-street Canyon |
Carrier Frequency | 30GHz |
Bandwidth | 2GHz |
BS antenna configurations | M = 8, N = 8, P = 2, Mg = 1, Ng = 4, dH = dV = 0.5λ, dH,g = dV,g = 4λ |
BS port mapping | all 64 elements for each polarization on each panel are mapped to a single CRS port; panning angles of the two subarrays: (0,0) degs; same downtilt angles as used for the large-scale calibrations |
The number of rays per cluster | NMPC = 40 |
Calibration method | Drop multiple users in the multiple cells randomly, and collect the following metrics for each user after attachment. |
Metrics | 1) CDF of coupling loss (serving cell) 2) Wideband SINR before receiver – determined from RSRP (formula) from CRS port 0 3) CDF of largest (1st) PRB singular values (serving cell) at t=0 plotted in 10*log10 scale |
Table 7.8-5: Simulation assumptions for calibration for spatial consistency
Parameter | Values |
Scenarios | UMi-street Canyon |
Carrier Frequency | 30 GHz |
BS antenna configurations | M = 4, N = 4, P = 2, Mg = 1, Ng = 2, dH = dV = 0.5λ, dH,g = dV,g = 2.5λ |
BS port mapping | all 16 elements for each polarization on each panel are mapped to a single CRS port; panning angles of the two subarrays: (0,0) degs; same downtilt angles as used for the large-scale calibrations |
UT distribution | Following TR36.873, 3D dropping uniform dropping for indoor with minimum distance of 0 m For Config1: 100% UT indoor and in the 1st floor For Config2: 100% UT outdoor |
Mobility | Config1: UT is stationary Config2: UT is moving with random direction and fixed speed, e.g., 30 km/h |
Calibration method | For Config1: Drop multiple UEs in a single cell, determine all permutations of pairs of UEs, collect the variables for each pair and bin them into certain distance ranges, e.g., 1m/2m/10m, to get enough samples. Collect the following metrics 1) –6). For Config2: Drop multiple users in the single cell, and collect metric 1)-2) and 7)-9) for each user after attachment. |
Metrics | 1) CDF of coupling loss (serving cell) 2) Wideband SINR before receiver – determined from RSRP (formula) from CRS port 0 3) Cross-correlation coefficient of delay for the third cluster between paired UEs, see note 1 4) Cross-correlation coefficient of AOA for the third cluster between paired UEs 5) Cross-correlation coefficient of LOS/NLOS status between paired UEs 6) Cross correlation coefficient of the channel response in Step 11 on the first non-DC subcarrier in an OFDM symbol on antenna port 0 received on the first UT antenna over multiple realizations between paired UEs 7) CDF of average varying rate of power for the third cluster, see note 2 8) CDF of average varying rate of delay for the third cluster (ns) 9) CDF of average varying rate of AOA for the third cluster (degree) |
NOTE 1 For the UT pair at a certain distance range, the variables collected by two UEs can be denoted as X and Y, respectively, then the cross-correlation coefficient for real number can be written as [E(XY)-E(X)E(Y)] /sqrt([E(X^2)-E(X)^2])/ sqrt([E(Y^2)-E(Y)^2]), the cross-correlation coefficient for complex number can be written as |[E(XY*)-E(X)E(Y)*]/sqrt([E(XX*)-E(X)E(X)*])/sqrt([E(YY*)-E(Y)E(Y)*])|. NOTE 2 For the average varying rate, we assume the collecting interval, e.g., 100ms, and then get the samples for a certain UT, the varying rate can be written as the standard variance of the samples per 100ms. | |
Table 7.8-6: Simulation assumptions for calibration for blockage
Parameter | Values |
Scenarios | UMi-street Canyon |
Carrier Frequency | 30 GHz |
BS antenna configurations | M = 4, N = 4, P = 2, Mg = 1, Ng = 2, dH = dV = 0.5λ, dH,g = dV,g = 2.5λ |
BS port mapping | all 16 elements for each polarization on each panel are mapped to a single CRS port; panning angles of the two subarrays: (0,0) degs; same downtilt angles as used for the large-scale calibrations |
Calibration method | For Model A: Drop multiple users in the multiple cells, and collect the following metrics 1) – 3) for each user after attachment. Optional self-blocking feature is made mandatory in the Landscape mode only for calibration purposes. For Model B: Drop a BS in (0,0,30) and a UT in (100,0,1.5), Jump directly to Step 11 and replace the channel with CDL-E. Drop a blocking screen of size h=10m, w = 2m in (80,10,1.5) Move the UT from (100,0,1.5) to (100,20,1.5) in small increments. For each UT position, translate all the AODs and AOAs of CDL-E such that the specular (LOS path) of CDL-E is pointing along the direct path between the BS and UT. Collect metric 4) |
Metrics | 1) CDF of coupling loss (serving cell) 2) Wideband SINR before receiver – determined from RSRP (formula) from CRS port 0 3) CDF of ASA from the serving cell 4) RSRP as a function of UT position |
Table 7.8-6A: Simulation assumptions for calibration for near field channel modeling
Parameter | Values | |
Scenarios | UMi-street canyon, Indoor-office (open office) | |
Sectorization | For UMi-street canyon: sectorized as described in Table 7.8-1. For indoor-office: non-sectorized, 1 BS per BS location. | |
Carrier Frequency | 7 GHz (optional) 15 GHz | |
Bandwidth | 20 MHz | |
BS antenna height | UMi-street Canyon: 10m Indoor-office: 3m | |
BS antenna downtilt | Electrical downtilt of 102 degrees for UMi-street canyon Mechanical downtilt of 180 degrees for indoor-office | |
BS Tx power | 44 dBm for UMi-street canyon 24 dBm for Indoor-office | |
BS antenna configurations and port mapping | For UMi-street canyon scenario: Config 1 for 7 GHz: (M, N, P, Mg, Ng; MP, NP) = (24, 32, 2, 1, 1; 8, 32), dH = 0.5λ, dV = 0.7λ; (optional) (M, N, P, Mg, Ng; MP, NP) = (64, 16, 2, 1, 1; 16, 16), dH = dV = 0.5λ Config 2 for 15 GHz: (M, N, P, Mg, Ng; MP, NP) = (64, 16, 2, 1, 1; 8, 16), dH = dV = 0.5λ
For Indoor-office scenario for 7 GHz: (M, N, P, Mg, Ng; MP, NP) = (8, 24, 2, 1, 1; 8, 8), dH = dV = 0.5λ (optional) (M, N, P, Mg, Ng; MP, NP) = (64, 16, 2, 1, 1; 16, 16), dH = dV = 0.5λ
Mp and Np are the number of vertical, horizontal TXRUs within a panel and polarization | |
BS Polarized antenna modeling | Model-2 in Clause 7.3.2 | |
UT Location | Outdoor/indoor | UMi-street canyon: 100% outdoor Indoor-office: 100% indoor |
LOS/NLOS | 100% LOS | |
UT antenna height | UMi-street canyon: 1.5m Indoor-office: 1m | |
UT antenna configurations | Config A: Mg = Ng = 1, M = 2, N = 1, P = 2, dH = dV = 0.5λ Config B: 4 antenna port with single/linear polarization for calibration based on handheld device antenna model using candidate antenna locations (1,7,3,5) as described in Clause 7.3 | |
UT antenna pattern | Isotropic | |
UT Polarized antenna modelling | For UT Config A: Model-2 in Clause 7.3.2 For UT Config B: following the clause 7.3 | |
UT orientation | Config A: ΩUT,α uniformly distributed on [0,360] degree, ΩUT,β= 90 degree, ΩUT,γ = 0 degree Config B: ΩUT,α uniformly distributed on [0,360] degree, ΩUT,β= 45 degree, ΩUT,γ = 0 degree | |
Applicability of Nearfield Modeling | Component of NF channel to be considered: - Phase of Direct path - Phase of Non-direct path, applied only at the BS side (optionally) | |
Calibration method | Drop multiple users in the multiple cells at the fixed horizontal distance Z from the BS, and collect the metric for For UMi-street Canyon: Z = 10, 30, 50, 100 m (optional) Z = 20, 40, 80 m
For Indoor: Z = 0, 2, 6, 10 m (optional) Z = 4, 8 m
| |
Metric | CDF of the ratio between the 2nd, 3rd ,4th, ..., xth (smallest, see note 1) PRB singular value and the 1st PRB (largest) singular value (see note 2) of the serving cell at t=0 plotted in 10*log10 scale. | |
NOTE 1: The value of x is the minimum value of (number of BS antenna ports, number of UT antenna ports) NOTE 2: The PRB singular values of a PRB are the eigenvalues of the mean covariance matrix in the PRB. | ||
Table 7.8-6B: Simulation assumptions for calibration for BS side spatial non-stationarity
Parameter | Values |
Scenarios | UMi-street Canyon |
Carrier Frequency | 7 GHz (optional) 15 GHz |
Bandwidth | 20 MHz |
BS Antenna height | 10m for UMi-Street Canyon |
BS Tx power | 44 dBm for UMi-Street Canyon |
BS Antenna Configuration and port mapping | Config 1 for 7 GHz: (M, N, P, Mg, Ng; MP, NP) = (24, 32, 2, 1, 1; 8, 32), dH = 0.5λ, dV = 0.7λ; (optional) (M, N, P, Mg, Ng; MP, NP) = (64, 16, 2, 1, 1; 16, 16), dH = dV = 0.5λ
Config 2 for 15 GHz: (M, N, P, Mg, Ng; MP, NP) = (64, 16, 2, 1, 1; 8, 16), dH = dV = 0.5λ
Mp and Np are the number of vertical, horizontal TXRUs within a panel and polarization |
BS Polarized antenna modeling | Model-2 in Clause 7.3.2 |
UT Antenna Configuration | Config 1: 4 antenna port with single/linear polarization for calibration based on handheld device antenna model using candidate antenna locations (1,7,3,5) as described in Clause 7.3 |
UT antenna pattern | Isotropic |
UT Polarized antenna modelling | Following the Clause 7.3 |
UT orientation | ΩUT,α uniformly distributed on [0,360] degree, ΩUT,β= 45 degree, ΩUT,γ = 0 degree |
UT attachment | Based on RSRP (formula) from BS port 0 |
UT distribution | Following TR36.873 for UMi, (3D dropping) |
Spatial non-stationarity modeling at the BS | Case 1: Spatial blocker-based approach (e.g. K = 1 per sector) Case 2: Stochastic-based approach |
Metric | (1) CDF of coupling loss (serving cell). 2) CDF of the ratio between the 2nd, 3rd ,4th, ..., xth (smallest, see note 1) PRB singular value and the 1st PRB (largest) singular value (see note 2) of the serving cell at t=0 plotted in 10*log10 scale. |
NOTE 1: The value of x is the minimum value of (number of BS antenna ports, number of UT antenna ports) NOTE 2: The PRB singular values of a PRB are the eigenvalues of the mean covariance matrix in the PRB. | |
Table 7.8-6C: Simulation assumptions for calibration for UT side spatial non-stationarity
Parameter | Values |
Scenarios | UMi-street Canyon |
Carrier Frequency | 7 GHz (optional) 15 GHz |
Bandwidth | 20 MHz |
BS Antenna height | 10m for UMi-Street Canyon |
BS Tx power | 44 dBm for UMi-Street Canyon |
BS Antenna Configuration and port mapping | (M, N, P, Mg, Ng; MP, NP) = (8, 16, 2, 1, 1; 8, 16), dH = dV =0.5λ Mp and Np are the number of vertical, horizontal TXRUs within a panel and polarization |
BS Polarized antenna modeling | Model-2 in Clause 7.3.2 |
UT Antenna Configuration | Config A for 7GHz: 4 antenna port with single polarization for calibration based on handheld device antenna model using candidate antenna locations (1, 7, 3, 5) as described in Clause 7.3 Config B for 15 GHz: 8 antenna port with single polarization based on handheld device antenna model using all candidate antenna locations (1,2,3,4,5,6,7,8) as described in Clause 7.3. |
UT antenna pattern | Based on directional antenna for UT described in Clause 7.3 for each supported UT antenna configuration |
UT Polarized antenna modelling | Following the Clause 7.3 |
UT orientation | Case with blockage: - For one-hand blockage, ΩUT,α = 0 – 360 deg, ΩUT,β = 45 deg, ΩUT,γ = 0 deg, - For dual-hand blockage, ΩUT,α = 0 – 360 deg, ΩUT,β = 0 deg, ΩUT,γ = 45 deg, - For hand and head blockage, ΩUT,α = 0 – 360 deg, ΩUT,β = 90 deg, ΩUT,γ = 0 deg, Case without blockage: - ΩUT,α = 0 – 360 deg, ΩUT,β = 45 deg, ΩUT,γ = 0 deg, |
UT attachment | Based on RSRP (formula) from BS port 0 |
UT distribution | Following TR36.873 for UMi, (3D dropping) |
Spatial non-stationarity modeling at the UT | Case 1: one hand grip Case 2: dual hand grip Case 3: head and one hand grip See note 3 |
Metric | CDF of coupling loss (serving cell) received per UT port CDF of the ratio between the 2nd, 3rd ,4th, ..., xth (smallest, see note 1) PRB singular value and the 1st PRB (largest) singular value (see note 2) of the serving cell at t=0 plotted in 10*log10 scale. |
NOTE 1: The value of x is the minimum value of (number of BS antenna ports, number of UT antenna ports) NOTE 2: The PRB singular values of a PRB are the eigenvalues of the mean covariance matrix in the PRB. NOTE 3: For each UT, the selection of case is determined by the probability defined in Table 7.5.14-1. Probability of applying blockage for UT is 90%. | |
7.8.4 Calibration of the indoor factory scenario #
For the InF, the calibration parameters can be found in Table 7.8-7. The calibration results can be found in R1-1909704.
It should be noted absolute delay model had not been agreed by the deadline, so companies were not able to submit CDF of first path excess delay for serving cell.
Table 7.8-7: Simulation assumptions for large scale calibration for the indoor factory scenario
Parameter | Values |
Scenario | InF-SL, InF-DL, InF-SH, InF-DH |
Hall size | InF-SL: 120x60 m InF-DL: 300x150 m InF-SH: 300x150 m InF-DH: 120x60 m |
Room height | 10 m |
Sectorization | None |
BS antenna configurations | 1 element (vertically polarized), Isotropic antenna gain pattern |
UT antenna configurations | 1 element (vertically polarized), Isotropic antenna gain pattern |
Handover margin (for calibration) | 0dB |
BS deployment | 18 BSs on a square lattice with spacing D, located D/2 from the walls. - for the small hall (L=120m x W=60m): D=20m - for the big hall (L=300m x W=150m): D=50m
BS height = 1.5 m for InF-SL and InF-DL BS-height = 8 m for for InF-SH and InF-DH |
UT distribution | uniform dropping for indoor with minimum 2D distance of 1 m UT height = 1.5 m |
UT attachment | Based on pathloss |
UT noise figure | 9 dB |
Carrier frequency | 3.5 GHz, 28 GHz |
Bandwidth | 100 MHz |
Clutter density: | Low clutter density: 20% High clutter density: 60% |
Clutter height: | Low clutter density: 2 m High clutter density: 6 m |
Clutter size: | Low clutter density: 10 m High clutter density: 2 m |
Metrics | 1) Coupling loss – serving cell |
2) Geometry with and without noise | |
3) CDF of delay spread and angle spread (ASD, ZSD, ASA, ZSA) according to the definition in Annex A.1 | |
4) CDF of first path excess delay for serving cell |
7 .9 Channel model(s) for ISAC #
7.9.0 Introduction #
The channel model for ISAC in Clause 7.9 is designed based on the channel model defined in the previous clauses within Clause 7 taking into account the known properties, e.g., location, Radar Cross-Section (RCS), polarization and etc. of one or more physical objects. A physical object is categorized as a sensing target (ST) or an environment object (EO). A ST is an object of interest for sensing. An EO is a non-target object with known location. Two types of EO are supported in the ISAC channel model. A first type of EO (type-1 EO) has similar characteristic as a ST and is modelled in the same way as a ST. In the following descriptions in Clause 7.9, only the related details on ST are described, which are also applicable to type-1 EO. A second type of EO (type-2 EO, 7.9.5.2) is of large size and is modelled differently from a ST.
The large scale and small scale parameters of the channel between any two of sensing transmitter (STX), ST and sensing receiver (SRX) in a sensing scenario are obtained from the Technical Reports for the same communication scenario unless updates on the parameter values are specially described. The ST is considered as a receiver or transmitter, respectively, in the determination of a proper channel model for a STX-ST link or a ST-SRX link.
The common framework for the ISAC channel between a pair of STX and SRX is composed of a component for one or multiple target channel(s) and a component for a background channel (described in Clause 7.9.4.3 and 7.9.5.3). One or multiple STs can be modelled in the channel between one pair of STX and SRX. The target channel of ST k includes all multipath components impacted by ST k,
7.9.1 Scenarios #
Sensing scenario X is defined as a scenario for sensing where STX/SRX are selected among the TRPs and UEs in the corresponding communication scenario X. X can be UMi/UMa/RMa/InH/InF/UMi-AV/UMa-AV/RMa-AV/Urban grid/Highway/High Speed Train (HST). Based on the interested type of ST, the sensing scenarios can be grouped to UAV sensing scenarios (ISAC-UAV), Automotive sensing scenarios (ISAC-Automotive), Human sensing scenarios (ISAC-Human), Automated Guided Vehicles (AGV) sensing scenarios (ISAC-AGV), and Objects creating hazards on roads/railways sensing scenarios (ISAC-Objects creating hazards on roads/railways).
The detailed sensing scenario description in this clause can be used for channel model calibration.
ISAC-UAV
In the ISAC-UAV sensing scenarios, the sensing targets are outdoor UAVs below or above the buildings in urban or rural areas. Monostatic or bistatic sensing can be performed using TRPs and/or UEs, including UEs on other UAVs. Details on ISAC-UAV scenarios are listed in Table 7.9.1-1.
Table 7.9.1-1: Evaluation parameters for UAV sensing scenarios
Parameters | Value | |
Applicable communication scenarios | UMi, UMa, RMa, SMa UMi-AV, UMa-AV, RMa-AV [36.777] | |
Sensing transmitters and receivers properties | STX/SRX Locations
| STX/SRX locations are selected among the TRPs and UEs locations in the corresponding communication scenarios. see note 1 |
Sensing target | LOS/NLOS | LOS and NLOS |
Outdoor/indoor | Outdoor | |
3D mobility | Horizontal velocity: uniform distribution between 0 and 180km/h, if horizontal velocity is not fixed to 0.
Vertical velocity: 0km/h, optional {20, 40} km/h see note 2 and 3 | |
3D distribution | Horizontal plane: Option A: N targets uniformly distributed within one cell. Option B: N targets uniformly distributed per cell. Option C: N targets uniformly distributed within an area not necessarily determined by cell boundaries. N = {1, 2, 3, 4, 5} see note 4
Vertical plane: Option A: Uniform between 1.5m and 300m. Option B: Fixed height value chosen from {25, 50, 100, 200, 300} m assuming vertical velocity is equal to 0. | |
Orientation | Random in horizontal domain | |
Physical characteristics (e.g., size) | Size: Option 1: 1.6m x 1.5m x 0.7m Option 2: 0.3m x 0.4m x 0.2m | |
Minimum 3D distances between STX/SRX and sensing target | Min distances based on min TRP/UE distances defined in TR36.777. | |
Minimum 3D distance between sensing targets | Option 1: At least larger than the physical size of a target Option 2: 10 meters | |
NOTE 1: This may include aerial UEs for UMi-AV, UMa-AV, RMa-AV communication scenarios. In this case, other STX/SRX properties (e.g. mobility) are also taken from the corresponding communication scenario. NOTE 2: 3D mobility can be horizontal only or vertical only or a combination for each sensing target NOTE 3: time-varying velocity may be considered for future evaluations. NOTE 4: N=0 may be considered for the evaluation of false alarm | ||
ISAC-Automotive
In the ISAC-Automotive sensing scenarios, the sensing targets are passenger vehicles or trucks and buses traveling on roads and streets in urban and rural areas. Monostatic or bistatic sensing can be performed using TRPs and/or UEs, including UEs on other vehicles and roadside UEs (RSU-type UEs). Details on ISAC-Automotive scenarios are listed in Table 7.9.1-2.
Table 7.9.1-2: Evaluation parameters for Automotive sensing scenarios
Parameters | Values | |
Applicable communication scenarios | Highway, Urban Grid. UMi, UMa, RMa, SMa | |
Sensing transmitters and receivers properties | STX/SRX locations are selected among the TRPs and UEs (e.g., VRU, vehicle, RSU-type UEs) locations in the corresponding communication scenario. Additional option: ISD between TRPs of Urban Grid is 250m | |
Sensing target | LOS/NLOS | LOS and NLOS (including NLOSv) |
Outdoor/indoor | Outdoor | |
Mobility (horizontal plane only) | Based on TR37.885 mobility for urban grid or highway scenario | |
Distribution (horizontal) | Based on dropping in TR37.885 per urban grid or highway communication scenario | |
Orientation | Lane direction in horizontal plane | |
Physical characteristics (e.g., size) | Type 1/2 (passenger vehicle) Type 3 (truck/bus) Vehicle type distribution per TR 37.885 | |
Minimum 3D distances between STX/SRX and sensing target | For Highway, Urban Grid - Min distances based on min TRP/UE distances defined in TR37.885 For UMi, UMa, RMa, SMa - Min distances based on min TRP/UE distances defined in TR38.901 | |
Minimum 3D distance between sensing targets | Option 1: At least larger than the physical size of a sensing target Option 2: Fixed value, 10 m. | |
Environment Objects, e.g., types, characteristics, mobility, distribution, etc. | Type-2 EO for Urban Grid - up to 4 walls modelled as Type-2 EO, per building of size 413m x 230m x 20m. | |
ISAC-Human
In the ISAC-Human sensing scenarios, the sensing targets are children and adult persons in indoor (room, office, factory) and outdoor (urban, rural) locations. Monostatic or bistatic sensing can be performed using TRPs and/or UEs in the corresponding communication scenarios. Details on ISAC-Human scenarios are listed in Table 7.9.1-3.
Table 7.9.1-3: Evaluation parameters for Human (indoor and outdoor) sensing scenarios
Parameters | Indoor Values | Outdoor Values | |
Applicable communication scenarios | Indoor office, indoor factory Indoor room [TR38.808] | UMi, UMa, RMa, SMa | |
Sensing transmitters and receivers properties | STX/SRX Locations | STX/SRX locations are selected among the TRPs and UE locations in the corresponding communication scenario | STX/SRX locations are selected among the TRPs and UE locations in the corresponding communication scenario |
STX/SRX Mobility for UEs | Option 1: 0km/h Option 2: 3km/h Option 3: Uniform distribution between 0km/h and 3km/h | Option 1: 0km/h Option 2: 3km/h Option 3: Uniform distribution between 0km/h and 10km/h | |
Sensing target | LOS/NLOS | LOS and NLOS | LOS and NLOS |
Outdoor/indoor | Indoor | Outdoor | |
3D mobility | Option 1: 0km/h Option 2: 3km/h Option 3: Uniform distribution between 0km/h and 3km/h (horizontal plane with random direction straight-line trajectory) | Option 1: 0km/h Option 2: 3km/h Option 3: Uniform distribution between 0km/h and 10km/h (horizontal plane with random direction straight-line trajectory) | |
3D distribution | N targets uniformly distributed over the horizontal area of the convex hull of the TRP deployment see note 1 | Option A: N targets uniformly distributed within one cell. Option B: N targets uniformly distributed per cell. Option C: N targets uniformly distributed within an area not necessarily determined by cell boundaries. see note 1 | |
Orientation | Random over the horizontal area | Random over the horizontal area | |
Physical characteristics (e.g., size) | Size (Length x Width x Height): Child: 0.2m x 0.3m x 1m Adult Pedestrian: 0.5m x 0.5m x 1.75m | Size (Length x Width x Height): Child: 0.2m x 0.3m x 1m Adult Pedestrian: 0.5m x 0.5m x 1.75m | |
Minimum 3D distances between STX/SRX and sensing target | For TRP monostatic and TRP-TRP bistatic sensing modes, the minimum 2D distance between STX/SRX and the sensing target is 0 m For UE monostatic and UE-UE bistatic sensing modes, the minimum 2D distance between STX/SRX and the sensing target is 1 m (as specified in TR 38.858) | For TRP monostatic and TRP-TRP bistatic sensing modes, the minimum 2D distances between STX/SRX and the sensing target are 10 m for UMi scenarios and 35 m for UMa scenarios For UE monostatic and UE-UE bistatic sensing modes, the minimum 2D distance between STX/SRX and the sensing target is 1 m (as specified in TR 38.858) | |
Minimum 3D distance between sensing targets | Option 1: At least larger than the physical size of a sensing target Option 2: Fixed value, 1m | Option 1: At least larger than the physical size of a sensing target Option 2: Fixed value, 1m | |
NOTE 1: N=0 may be considered for the evaluation of false alarm | |||
ISAC-AGV
In the ISAC-AGV sensing scenarios, the sensing targets are automated guided vehicles (AGVs) inside a factory. Monostatic or bistatic sensing can be performed using TRPs and/or UEs in the corresponding communication scenario. Details on ISAC-AGV sensing scenarios are listed in Table 7.9.1-4.
Table 7.9.1-4: Evaluation parameters for AGV sensing scenarios
Parameters | Value | |
Applicable communication scenarios | InF (Table 7.8-7) | |
Sensing transmitters and receivers properties | STX/SRX location are selected among the TRPs and UEs location in the corresponding communication scenario
STX/SRX Mobility for UEs Option 1: 0 km/h Option 2: 3km/h Option 3: Uniform distribution between 0km/h and 3km/h | |
Sensing target | LOS/NLOS | LOS and NLOS |
Outdoor/indoor | Indoor | |
3D mobility | Horizontal velocity with random straight-line trajectory Option 1: Uniform distribution in the range of up to 30 km/h Option 2: Fixed velocities {3, 10} km/h | |
3D distribution | Option A: Uniformly distributed in the convex hull of the horizontal BS deployment Option B: Uniformly distributed in horizontal plane | |
Orientation | Horizontal plane only | |
Physical characteristics (e.g., size) | Size Option 1: 0.5m x 1.0m x 0.5m Option 2: 1.5 m x 3.0m x 1.5 m | |
Minimum 3D distances between STX/SRX and sensing target | Min distances based on min TRP/UE distances defined in TR38.901 | |
Minimum 3D distance between sensing targets | Option A: At least larger than the physical size of a target Option B: Fixed value, 5m | |
ISAC-Objects creating hazards on roads/railways
In the ISAC-Objects creating hazards on roads/railways sensing scenarios, the sensing targets are adult humans and children and animals in communication scenarios involving vehicles or high-speed trains. Monostatic or bistatic sensing can be performed using TRPs and/or UEs, including UEs on other vehicles and roadside UEs (RSU-type UEs). Details on ISAC- Objects creating hazards on roads/railways sensing scenarios are listed in Table 7.9.1-5.
Table 7.9.1-5: Evaluation parameters for objects creating hazards on roads/railways sensing scenarios
Parameters | Value | |
Applicable communication scenarios | Highway, Urban grid, HST (High Speed Train, TR 38.802) UMi, UMa, RMa, SMa | |
Sensing transmitters and receivers properties | STX/SRX Locations | STX/SRX locations are selected among the TRPs and UEs (e.g., VRU, vehicle, RSU-type UEs) locations in the corresponding communication scenarios. Additional option: ISD between TRPs of Urban Grid is 250 m |
Sensing target | LOS/NLOS | LOS and NLOS |
Outdoor/indoor | Outdoor | |
3D mobility | Horizontal velocity: up to 10 km/h for humans and animals | |
3D distribution | Uniformly distributed in horizontal plane | |
Orientation | Random distribution in horizontal plane | |
Physical characteristics (e.g., size) | For human/pedestrians: Child: 0.2m x 0.3m x 1m Adult: 0.5m x 0.5m x 1.75m For animals: Size: 1.5m x 0.5m x 1 m | |
Minimum 3D distances between STX/SRX and sensing target | For Highway, Urban Grid, HST - Min. distance is based on min TRP/UE distances defined in TR37.885 and TR38.802 For UMi, UMa, RMa, SMa - Min distances based on min TRP/UE distances defined in TR38.901 | |
Minimum 3D distance between sensing targets | Option 1: At least larger than the physical size of a sensing target Option 2: Fixed value, 10 m. | |
Environment objects, e.g., types, characteristics, mobility, distribution, etc. | Type-2 EO for Urban Grid - up to 4 walls modelled as Type-2 EO, per building of size 413m x 230m x 20m. | |
7.9.2 Physical object model #
7.9.2.0 Introduction #
A ST is modelled with one or multiple scattering points. Each scattering point of a ST (SPST) is used to model the total scattering effects of some adjacent scattering centres at the ST. The impact of a SPST to the channel includes at least two aspects, i.e., the RCS (Radar Cross Section, 7.9.2.1) and the polarization matrix (7.9.2.2).
7.9.2.1 RCS of a sensing target #
The RCS of a SPST is a scalar value defined in LCS of the ST and is dependent on both the incident angle and the scattered angle. The RCS values with same incident/scattered angles can be referred as monostatic RCS values.
The RCS related coefficient
A first RCS model, called RCS model 1, is to characterize a ST as single SPST with component
where,
-
-
The logarithmic values of
Table 7.9.2.1-1: Parameters on RCS for the STs with angular independent monostatic RCS values
Sensing target | (dBsm) | (dB) |
UAV with small size | -12.81 | 3.74 |
Human with RCS model 1 | -1.37 | 3.94 |
A second RCS model, called RCS model 2, adopts an angular dependent component
- The face of a human, the front of a vehicle, a UAV with large size or an AGV is facing the direction with azimuth angle
- The top of the ST is facing the direction with zenith angle
For UAV of large size with single SPST, human with RCS model 2 with single SPST, vehicle with single/multiple SPSTs, and AGV with single/multiple SPSTs, the values/pattern
With
where,
- (
-
-
-
-
-
-
For a ST with single scattering point,
The
Table 7.9.2.1-2: Parameters on RCS for UAV with large size
| (dBsm) | (dB) | ||||||||
Range of | Range of | |||||||||
Left | 90 | 7.13 | 90 | 8.68 | 7.43 | 14.30 | [45,135) | [45,135) | -5.85 | 2.50 |
Back | 180 | 10.09 | 90 | 11.43 | 3.99 | 10.86 | [45,135) | [135,225) | ||
Right | 270 | 7.13 | 90 | 8.68 | 7.43 | 14.30 | [45,135) | [225,315) | ||
Front | 0 | 14.19 | 90 | 16.53 | 1.02 | 7.89 | [45,135) | [-45,45) | ||
Bottom | - | - | 180 | 4.93 | 13.55 | 20.42 | [135,180] | [0,360) | ||
Roof | - | - | 0 | 4.93 | 13.55 | 20.42 | [0,45) | [0,360) | ||
NOTE: When | ||||||||||
Table 7.9.2.1-3: Parameters on RCS for human with RCS model 2
| (dBsm) | (dB) | ||||||||
Range of | Range of | |||||||||
Front | 0 | 216.65 | 90 | 55.7 | 2.14 | 7.7 | [0,180] | [-90, 90) | -1.37 | 3.94 |
Back | 180 | 216.65 | 90 | 55.7 | 2.14 | 7.7 | [0,180] | [90,270) | ||
Table 7.9.2.1-4: Parameters on RCS for vehicle with single scattering point
| (dBsm) | (dB) | ||||||||
Range of | Range of | |||||||||
Left | 90 | 26.90 | 79.70 | 44.42 | 20.75 | 13.68 | [30,180] | [45,135) | 11.25 | 3.41 |
Back | 180 | 36.32 | 79.65 | 36.73 | 14.56 | 7.50 | [30,180] | [135,225) | ||
Right | 270 | 26.90 | 79.70 | 44.42 | 20.75 | 13.68 | [30,180] | [225,315) | ||
Front | 0 | 40.54 | 71.75 | 29.13 | 15.52 | 8.45 | [30,180] | [-45, 45) | ||
Roof | - | - | 0.00 | 18.13 | 21.26 | 14.19 | [0,30) | [0,360) | ||
NOTE: When | ||||||||||
Table 7.9.2.1-5: Parameters on RCS for vehicle with multiple scattering points
| (dBsm) | (dB) | ||||||||
Range of | Range of | |||||||||
Left | 90 | 26.90 | 79.70 | 44.42 | 20.60 | 20.52 | [0,180] | [0,360) | 11.25 | 3.41 |
Back | 180 | 36.32 | 79.65 | 36.73 | 13.90 | 13.82 | [0,180] | [0,360) | ||
Right | 270 | 26.90 | 79.70 | 44.42 | 20.60 | 20.52 | [0,180] | [0,360) | ||
Front | 0 | 40.54 | 71.75 | 29.13 | 14.99 | 14.91 | [0,180] | [0,360) | ||
Roof | - | - | 0.00 | 18.13 | 21.12 | 21.05 | [0,180] | [0,360) | ||
NOTE: For the scattering point associated with roof of the vehicle, | ||||||||||
Table 7.9.2.1-6: Parameters on RCS for AGV with single scattering point
| (dBsm) | (dB) | ||||||||
Range of | Range of | |||||||||
Front | 0 | 13.68 | 90 | 13.68 | 13.02 | 23.29 | [30,180] | [-45,45) | -4.25 | 2.51 |
Left | 90 | 15.53 | 75 | 20.03 | 7.33 | 17.60 | [30,180] | [45,135) | ||
Back | 180 | 12.49 | 90 | 11.89 | 11.01 | 21.28 | [30,180] | [135,225) | ||
Right | 270 | 15.53 | 75 | 20.03 | 7.33 | 17.60 | [30,180] | [225,315) | ||
Roof | - | - | 0 | 11.44 | 11.79 | 22.06 | [0,30) | [0,360) | ||
NOTE: When | ||||||||||
Table 7.9.2.1-7: Parameters on RCS for AGV with multiple scattering points
| (dBsm) | (dB) | ||||||||
Range of | Range of | |||||||||
Front | 0 | 13.68 | 90 | 13.68 | 13.00 | 30.26 | [0,180] | [0,360) | -4.25 | 2.51 |
Left | 90 | 15.53 | 75 | 20.03 | 7.27 | 24.53 | [0,180] | [0,360) | ||
Back | 180 | 12.49 | 90 | 11.89 | 10.98 | 28.24 | [0,180] | [0,360) | ||
Right | 270 | 15.53 | 75 | 20.03 | 7.27 | 24.53 | [0,180] | [0,360) | ||
Roof | - | - | 0 | 11.44 | 11.77 | 29.03 | [0,180] | [0,360) | ||
NOTE: For the scattering point associated with roof of the AGV, | ||||||||||
7.9.2.2 Cross-polarization matrix of a sensing target #
The cross-polarization matrix
For UAV, human, vehicle and AGV,
where,
-
-
Table 7.9.2.2-1: Parameters on XPR (dB) for the targets
Target | μXPR | σXPR |
UAV | 13.75 | 7.07 |
Human | 19.81 | 4.25 |
Vehicle | 21.12 | 6.88 |
AGV | 9.60 | 6.85 |
7.9.3 Reference channel models and required updates #
A transmitter or receiver in the sensing operation can be TRP, terrestrial UE, vehicle UE, aerial UE, AGV UE or RSU-type UE. The reference TR(s) to generate the channel for each combination of transmitter and receiver for each sensing scenario are provided in Table 7.9.3-1, where the terrestrial UE, AGV are referred as terrestrial UE.
Table 7.9.3-1: Reference TRs to generate channel for ISAC
Case | Tx/Rx | Rx/Tx | Reference TR to define the channel model |
1 | TRP | TRP | For sensing scenario UMi/UMi-AV, UMa/UMa-AV, RMa/RMa-AV, InH, InF - TRP-TRP link of scenario UMi, UMa, InH, and InF following the option based on TR 38.901 defined in Clause A.3 of TR 38.858 - For InF, hUE is changed to the same height as the BS - TRP-UE link of scenario RMa defined in Clause 7 of TR 38.901 by setting hUE=35m (see note 1) For sensing scenario Highway - TRP-UE link of scenario RMa in Clause 7 of TR 38.901 by setting hUE=35m for FR1(see note 1) - TRP-TRP link of scenario UMa following the option based on TR 38.901 defined in Clause A.3 of TR 38.858 For sensing scenario Urban grid - TRP-TRP link of scenario UMa following the option based on TR 38.901 defined in Clause A.3 of TR 38.858 For sensing scenario HST - TRP-UE link of scenario RMa in Clause 7 of TR 38.901 by setting hUE=35m for FR1 (see note 1) - TRP-TRP link of scenario UMa following the option based on TR 38.901 defined in Clause A.3 of TR 38.858 for FR2 |
2 | TRP | terrestrial UE | For sensing scenario UMi/UMi-AV, UMa/UMa-AV, RMa/RMa-AV, InH, InF - TRP-UE link of scenario UMi, UMa, RMa, InH, and InF in Clause 7 of TR 38.901 For sensing scenario Highway and Urban grid - P2B link of scenario Highway and Urban grid in Clause 6 of TR 37.885 For sensing scenario HST - TRP-UE link of scenario RMa in Clause 7 of TR 38.901 for FR1 and TRP-UE link of scenario UMa in Clause 7 of TR 38.901 for FR2 |
3 | TRP | vehicle UE | For sensing scenario Highway and Urban grid - V2B link of scenario Highway and Urban grid in Clause 6 of TR 37.885 For sensing scenario UMi, UMa, and RMa - TRP-UE link of scenario UMi, UMa, and RMa in Clause 7 of TR 38.901 |
4 | TRP | aerial UE | For sensing scenario UMa-AV, UMi-AV, and RMa-AV - TRP-aerial UE link of scenario UMa-AV, UMi-AV, and RMa-AV in Clause Annex A and B of TR 36.777 for FR1 - Reuse the channel model of scenario UMa-AV, UMi-AV, and RMa-AV of FR1 for FR2 |
5 | terrestrial UE | terrestrial UE | For sensing scenario UMi/UMi-AV, UMa/UMa-AV, RMa/RMa-AV, InH, InF - UE-UE link of scenario UMi, UMa, InH, and InF following the option based on TR 38.901 defined in Clause A.3 of TR 38.858 - TRP-UE link of scenario RMa defined in Clause 7 of TR 38.901 by setting hBS =1.5m (see note 2) For sensing scenario Highway and Urban grid - P2P link in Clause 6 of TR 37.885 For sensing scenario HST - TRP-UE link of scenario RMa in Clause 7 of TR 38.901 for FR1, e.g., hBS=1.5m, - UE-UE link of scenario UMa following the option based on TR 38.901 defined in Clause A.3 of TR 38.858 for FR2 |
6 | terrestrial UE | vehicle UE | For sensing scenario UMi, UMa, RMa - UE-UE link of scenario UMi, UMa following the option based on TR 38.901 defined in Clause A.3 of TR 38.858 - TRP-UE link of scenario RMa defined in Clause 7 of TR 38.901 by setting hBS =1.5m For sensing scenario Highway and Urban grid - V2P link in Clause 6 of TR 37.885 |
7 | terrestrial UE | aerial UE | For sensing scenario UMi-AV, UMa-AV, and RMa-AV - TRP-aerial UE link of UMi-AV in Annex A and B of TR 36.777 by setting hBS =1.5m for FR1 - Reuse the channel model of scenario UMa-AV, UMi-AV, and RMa-AV of FR1 for FR2 - The corresponding parameter values in FR2 are used |
8 | vehicle UE | vehicle UE | For sensing scenario Highway and Urban grid - V2V link of scenario Highway and Urban grid in Clause 6 of TR 37.885 For sensing scenario UMi, UMa, and RMa - UE-UE link of scenario UMi, UMa following the option based on TR 38.901 defined in Clause A.3 of TR 38.858 - TRP-UE link of scenario RMa defined in Clause 7 of TR 38.901 by setting hBS =1.5m |
9 | aerial UE | aerial UE | For sensing scenario UMi-AV, UMa-AV, RMa-AV - TRP-aerial UE link of UMi-AV in Annex A and B of TR 36.777 by setting height of TRP equal to the height of the first aerial UE for FR1 (see note 4) - Reuse the channel model of scenario UMa-AV, UMi-AV, and RMa-AV of FR1 for FR2 - The corresponding parameter values in FR2 are used |
10 | TRP | RSU-type UE | Highway and Urban grid - B2R link in Clause 6 of TR 37.885 |
11 | RSU-type UE | normal UE | Highway and Urban grid - V2V link in Clause 6 of TR 37.885, with antenna height at RSU is 5m |
12 | RSU-type UE | RSU-type UE | Highway and Urban grid - V2V link in Clause 6 of TR 37.885, with antenna height at RSU is 5m |
13 | RSU-type UE | vehicle UE | Highway and Urban grid - V2V link in Clause 6 of TR 37.885, with antenna height at RSU is 5m |
N OTE 1: ASA and ZSA statistics updated to be the same as ASD and ZSD; ZoD offset = 0 N OTE 2: ASD and ZSD statistics updated to be the same as ASA and ZSA NOTE 3: Indoor office scenario can be categorized into 5 sub-indoor scenarios defined in TR38.808. NOTE 4: First aerial UE height is | |||
The large scale and small scale parameters of the STX-ST link and the ST-SRX link for a sensing scenario are determined according to Table 7.9.3-1 assuming the same sensing scenario. The targets of UAV and vehicle are respectively considered as an aerial UE and vehicle UE. Other targets are considered as terrestrial UE. The proper case for each combination of STX/STX and ST are provided in Table 7.9.3-2.
Table 7.9.3-2: Channel model for STX-ST and ST-SRX link
STX/SRX | Target | Cases in Table 7.9.3-1 assuming the same sensing scenario |
TRP | UAV | Case 4: TRP-Aerial UE link |
Human | Case 2: TRP-normal UE link | |
Vehicle | Case 3: TRP-Vehicle UE link | |
AGV | Case 2: TRP-normal UE link | |
Object (Hazard) | Case 2: TRP-normal UE link | |
Terrestrial UE | UAV | Case 7: normal UE - Aerial UE link |
Human | Case 5: normal UE-normal UE link | |
Vehicle | Case 6: normal UE - Vehicle UE link | |
AGV | Case 5: normal UE-normal UE link | |
Object (Hazard) | Case 5: normal UE-normal UE link | |
Vehicle UE | Human | Case 6: normal UE - Vehicle UE link |
Vehicle | Case 8: Vehicle UE - Vehicle UE link | |
Object (Hazard) | Case 6: normal UE - Vehicle UE link | |
Aerial UE | UAV | Case 9: Aerial UE - Aerial UE link |
AGV UE | AGV | Case 5: normal UE - normal UE link |
RSU-type UE | Human | Case 11: RSU-type UE - normal UE link |
Vehicle | Case 13: RSU-type UE - vehicle UE link |
The large scale and small scale parameters of the STX-SRX link, i.e., background channel for a sensing scenario are determined according to Table 7.9-1 assuming the same sensing scenario. The proper case for each combination of STX and SRX are provided in Table 7.9-3. For TRP monostatic sensing mode, a reference point (RP) is considered as a terrestrial UE. For UT monostatic sensing mode, a RP is considered as a TRP and an aerial UE is considered as a terrestrial UE.
Table 7.9.3-3: Channel model for background channel
STX/SRX | SRX/STX | Cases in Table 7.9.3-1 assuming the same sensing scenario |
TRP | TRP | Case 1: TRP-TRP link |
Terrestrial UE | Case 2: TRP-normal UE link | |
Vehicle UE | Case 3: TRP-Vehicle UE link | |
Aerial UE | Case 4: TRP-Aerial UE link | |
AGV UE | Case 2: TRP-normal UE link | |
RSU-type UE | Case 10: TRP - RSU-type UE link | |
Terrestrial UE | Terrestrial UE | Case 5: normal UE-normal UE link |
Vehicle UE | Case 6: normal UE - Vehicle UE link | |
Aerial UE | Case 7: normal UE - Aerial UE link | |
AGV UE | Case 5: normal UE -normal UE link | |
RSU-type UE | Case 11: RSU-type UE - normal UE link | |
Vehicle UE | Vehicle UE | Case 8: Vehicle UE - Vehicle UE link |
RSU-type UE | Case 13: RSU-type UE - vehicle UE link | |
Aerial UE | Aerial UE | Case 9: Aerial UE - Aerial UE link |
AGV UE | AGV UE | Case 5: normal UE - normal UE link |
RSU-type UE | RSU-type UE | Case 12: RSU-type UE – RSU-type UE link |
The following updates compared to the reference TRs are identified to generate ISAC channel.
- For human as a sensing target with a single scattering point, the height of the scattering point is 1.5 m.
- For sensing scenario RMa, UMi-AV, UMa-AV and RMa-AV, the height of a scattering point of a target is used to calculate the LOS probability and pathloss, regardless of the lower bound of hUT in the reference TRs that are used to generate ISAC channel.
- For sensing scenario UMi, UMa, the height of a scattering point of a target is used to calculate the LOS probability and pathloss, regardless of the lower bound of hUT in the reference TRs that are used to generate ISAC channel, except that hUT=1.5 m is assumed for breakpoint distance (dBP) calculation.
- For sensing scenario Urban grid, the absolute delay model of scenarios UMa in Table 7.6.9-1 are reused
- For sensing scenario highway and HST, the absolute delay model of scenarios RMa and UMa are reused for FR1 and FR2 respectively
- For sensing scenarios UMi-AV, UMa-AV and RMa-AV,
- For the TRP-TRP link and TRP-terrestrial UE link, the absolute delay model of scenarios UMi, UMa and RMa are respectively reused.
- For the terrestrial UE- terrestrial UE link, the terrestrial UE - aerial UE link and the aerial UE - aerial UE link, the absolute delay model of scenario UMi is reused.
- For the TRP- aerial UE link, the absolute delay model of scenarios UMi, UMa and RMa are respectively reused.
- On Case 7 in Table 7.9.3-1, the LOS probability between an aerial UE and a normal UE is generated by Table 7.9.3-4.
- On Case 9 in Table 7.9.3-1, the LOS probability between two aerial UEs is generated by Table 7.9.3-5.
Table 7.9.3-4: LOS condition determination for Case 7
Reference scenario to determined LOS probability | Applicability range in terms of aerial UE height |
UMi in Table 7.4.2-1 in TR 38.901 for sensing scenario UMi-AV/UMa-AV/RMa-AV | Sensing scenario UMi-AV, UMa-AV: Sensing scenario RMa-AV: |
UMi-AV in Table B-1 in TR 36.777 for sensing scenario UMi-AV/UMa-AV RMa-AV in Table B-1 in TR 36.777 for sensing scenario RMa-AV | Sensing scenario UMi-AV, UMa-AV: Sensing scenario RMa-AV: |
Table 7.9.3-5: LOS condition determination for Case 9
Reference scenario to determined LOS probability | Applicability range in terms of aerial UE height |
UMi in Table 7.4.2-1 in TR 38.901 for sensing scenario UMi-AV/UMa-AV/RMa-AV | Sensing scenario UMi-AV, UMa-AV: Sensing scenario RMa-AV: |
UMi-AV in Table B-1 in TR 36.777 for sensing scenario UMi-AV/UMa-AV RMa-AV in Table B-1 in TR 36.777 for sensing scenario RMa-AV | Sensing scenario UMi-AV, UMa-AV: Sensing scenario RMa-AV: |
UMa-AV in Table B-1 in TR 36.777 for aerial UE height in range (22.5m, 100m] for sensing scenario UMi-AV/UMa-AV RMa-AV in Table B-1 in TR 36.777 for aerial UE height in range (10m, 40m] for sensing scenario RMa-AV | Sensing scenario UMi-AV, UMa-AV: Sensing scenario RMa-AV: 1 |
LOS probability is 100% | Sensing scenario UMi-AV, UMa-AV: Sensing scenario RMa-AV: |
7.9.4 Fast fading model #
7.9.4.0 Introduction #
The ISAC channel between a pair of STX and SRX is composed of the target channel(s) and the background channel. A multipath component in the target channel may include stochastic generated clusters in either of or both the STX-ST link and ST-SRX link. If blockage/forward scattering between sensing targets is not considered, a propagation path from STX to SRX interacting with more than one sensing target is not modelled.
A stepwise procedure illustrated in Figure 7.9.4-1 is used to generate the channel model for ISAC referring to the procedure in Clause 7.5 with parameters derived by Table 7.9.3-2 and 7.9.3-3. Step 1 is commonly executed, followed by the respective steps for target channel (7.9.4.1) and background channel (7.9.4.2). Finally, the target channel and background channel are combined to form the final channel model for ISAC (7.9.4.3).
Figure 7.9.4-1 Channel coefficient generation procedure
Step 1: Set environment, network layout, and antenna array parameters
a) Choose one of the sensing scenarios (ISAC-UAV, ISAC-Automotive, ISAC-Human, ISAC-AGV, ISAC-Objects creating hazards on roads/railways) and related communication scenarios (e.g. UMa, UMi-Street Canyon, RMa, InH-Office, Urban grid, Highway, or InF). Choose a global coordinate system and define zenith angle θ, azimuth angle ϕ, and spherical basis vectors
b) Give number of STX, ST and SRX. Give the number of the SPST(s) of each ST.
c) Give 3D locations of STX and SRX, give 3D locations of SPST(s) of each ST, and determine LOS AOD (ϕLOS,AOD), LOS ZOD (θLOS,ZOD), LOS AOA (ϕLOS,AOA), and LOS ZOA (θLOS,ZOA) of each pair of STX and SPST, and each pair of SPST and SRX in the global coordinate system
d) Give STX and SRX antenna field patterns Frx and Ftx in the global coordinate system and array geometries.
e) Give STX and SRX array orientations with respect to the global coordinate system. STX array orientation is defined by three angles ΩSTX,α (STX bearing angle), ΩSTX,β (STX downtilt angle) and ΩSTX,γ (STX slant angle). SRX array orientation is defined by three angles ΩSRX,α (SRX bearing angle), ΩSRX,β (SRX downtilt angle) and ΩSRX,γ (SRX slant angle). Give the orientation of ST in the global coordinate system. ST orientation is defined by three angles ΩST,α (ST bearing angle), ΩST,β (ST downtilt angle) and ΩST,γ (ST slant angle).
f) Give speed and direction of motion of STX, ST and SRX in the global coordinate system
g) Specify system centre frequency
NOTE: In case wrapping is used, each wrapping copy of a STX or SRX should be treated as a separate STX/SRX considering channel generation.
For monostatic sensing mode, a STX and a corresponding SRX are co-located, and have the same antenna field pattern, array orientations, speed and direction of motion.
7.9.4.1 Target channel #
Following Step 1 in Clause 7.9.4.0, the target channels for the one or multiple STs between a pair of STX and SRX is generated using the following procedure with parameters derived by Table 7.9.3-2. It assumes a ST k consists of P scattering points indexed
Large scale parameters:
Step 2: Assign propagation condition (LOS/NLOS) for each pair of STX and SPST, and each pair of SPST and SRX according to LOS probability equation defined in reference TRs in Clause 7.9.3.
Step 3: Calculate pathloss with formulas defined in reference TRs in Clause 7.9.3 for each STX-SPST link, and each SPST-SRX link.
Step 4: For each STX-SPST link and SPST-SRX link, generate large scale parameters, e.g. delay spread (DS), angular spreads (ASA, ASD, ZSA, ZSD), Ricean K factor (K) and shadow fading (SF) taking into account cross correlation according to Table 7.5-6 and using the procedure described in clause 3.3.1 of [14] with the square root matrix
The LSPs for links from co-sited sectors to a STX/SPST/SRX are the same. In addition, these LSPs for the links of STX/SPST/SRX on different floors are uncorrelated.
Limit random RMS azimuth arrival and azimuth departure spread values to 104 degrees, i.e., ASA= min(ASA, 104), ASD = min(ASD, 104). Limit random RMS zenith arrival and zenith departure spread values to 52 degrees, i.e., ZSA = min(ZSA, 52), ZSD = min(ZSD, 52).
For monostatic sensing mode, the large scale parameters generated in step 2 to Step 4 are identical for a STX-SPST link and the corresponding SPST-SRX link of the same SPST.
Small scale parameters:
Step 5: Generate cluster delays
The cluster delays of cluster n in a STX-SPST link are generated using Step 5 of Clause 7.5, i.e.,
The cluster delays of cluster
Step 6: Generate cluster powers.
The cluster power of cluster n in a STX-SPST link are generated using Step 6 of Clause 7.5, i.e.,
The cluster power of cluster
Step 7: Generate arrival angles and departure angles for both azimuth and elevation.
The arrival angles and departure angles for both azimuth and elevation of a cluster n in a STX-SPST link are generated using Step 7 of Clause 7.5, i.e.,
The arrival angles and departure angles for both azimuth and elevation of a cluster n’ in the corresponding SPST-SRX link of the same SPST are generated using Step 7 of Clause 7.5 by replacing subscript n, m with n’, m’, i.e.,
For monostatic sensing mode,
Step 8: Coupling of rays within a cluster for both azimuth and elevation.
The rays within a cluster n for both azimuth and elevation in a STX-SPST link are coupled using Step 8 of Clause 7.5.
The rays within a cluster n’ for both azimuth and elevation in the corresponding SPST-SRX link of the same SPST are coupled using Step 8 of Clause 7.5 by replacing subscript n, m with n’, m’.
For monostatic sensing mode, the same ray-coupling way applies to both the STX-SPST link and the SPST-SRX link.
Step 9: Coupling of rays for a STX-SPST link and the corresponding SPST-SRX link of the same SPST.
In the STX-SPST link, a LOS ray is represented by
The LOS ray (if present) in the STX-SPST link is coupled with the LOS ray (if present) in the SPST-SRX link. The resulting path is never dropped. Further, a set
- The LOS ray in the STX-SPST link (if present) is coupled with each NLOS ray in the SPST-SRX link.
- Each NLOS ray in the STX-SPST link is coupled with the LOS ray (if present) in the SPST-SRX link.
- The NLOS rays in the STX-SPST link are coupled with the NLOS rays in the SPST-SRX link. Two options for the coupling are recommended with other methods for complexity reduction up to company choice.
- Option 1: Each NLOS ray in the STX-SPST link is coupled with each NLOS ray in the SPST-SRX link.
- Option 2: The NLOS rays in the STX-SPST link are 1-by-1 randomly coupled with the NLOS rays in the ST-SRX link. If the number of rays in the two links M1, M2 are not equal, min(M1, M2) rays are randomly selected in the link with larger number of rays in the coupling operation.
Step 10: Obtain the power for all generated paths
The power of a path (
where,
NOTE: The transformation between GCS and LCS for the incident/scattered angles is necessary to determine the RCS at the SPST p.
- If the STX-SPST link is in LOS condition,
- for a LOS ray
- for a NLOS ray m of a stochastic cluster n,
- If the STX-SPST link is not in LOS condition,
- for a NLOS ray m of a stochastic cluster n,
- If the SPST-SRX link is in LOS condition,
- for a LOS ray,
- for a NLOS ray m’ of a stochastic cluster n’,
- If the SPST-SRX link is not in LOS condition,
- for a NLOS ray m’ of a stochastic cluster n’,
A path with power less than X dB compared to the maximum power among all paths in set
Step 11: Obtain the absolute delay for each path in set R
The absolute delay of a path (
where, for the LOS ray in the STX-SPST link, if present,
For monostatic sensing mode,
Step 12: Generate the cross polarization power ratios for paths in set R.
The cross polarization power ratios for each ray m of a cluster n in a STX-SPST link is generated using Step 9 of Clause 7.5, i.e.,
The cross polarization power ratios for each ray m’ of a cluster n’ in a SPST-SRX link is generated using Step 9 of Clause 7.5 by replacing subscript n, m with n’, m’, i.e.,
For monostatic sensing mode,
Generate the cross polarization power ratios (XPR)
where
The outcome of Steps 1-12 shall be identical for all the links from co-sited sectors to a STX/ST/SRX.
Coefficient generation:
Step 13: Draw initial random phases for paths in set R.
The random initial phases for each ray m of a cluster n in a STX-ST link is generated using Step 9 of Clause 7.5, i.e.,
The random initial phases for each ray m’ of a cluster n’ in a ST-SRX link is generated using Step 9 of Clause 7.5 by replacing subscript n, m with n’, m’, i.e.,
For monostatic sensing mode,
Draw random initial phases
Step 14: Generate channel coefficients for paths in set R for each receiver and transmitter element pair u, s.
The channel coefficient for a path
with the Doppler frequency
where,
-
-
- for the LOS ray, if present,
- for NLOS ray generated by stochastic cluster,
-
- for the LOS ray, if present,
- for NLOS ray generated by stochastic cluster,
-
-
-
-
-
-
-
-
The channel impulse response of SPST p of ST k is given by
Step 15: Apply pathloss, shadowing, the first component of RCS for the channel coefficients.
The target channel for ST k,
where,
-
-
-
-
-
-
7.9.4.2 Background channel #
For TRP-TRP, TRP-UE, UE-TRP and UE-UE bistatic sensing modes, following Step 1 in Clause 7.9.4.0, the background channel between a pair of STX and SRX is generated using Step 2 to Step12 of Clause 7.5 with parameters derived by Table 7.9.3-3. The absolute time of arrival in clause 7.6.9 is applied. The resulting channel is denoted as
For TRP monostatic and UE monostatic sensing modes, the background channel between a pair of STX and SRX is generated using the following steps after Step 1 in Clause 7.9.4.0.
Step 2: Generate 3 reference points (RPs) for the STX/SRX.
Draw the 2D distance between the STX/SRX and each RP and the height of each RP respectively
- Determine LOS ZOD (θLOS,ZOD) of the STX/SRX and each RP in the global coordinate system.
- Set each RP the same antenna field patterns Frx in the global coordinate system and array geometries as the STX/SRX.
- Set each RP the same array orientations with respect to the global coordinate system, i.e., ΩRP,α (RP bearing angle), ΩRP,β (RP downtilt angle) and ΩRP,γ (RP slant angle) as the STX/SRX.
- Set each RP the same velocity as the STX/SRX.
NOTE: Distributions of height and distance of RPs are not subject to geographical constraints on TRP and UT for the corresponding deployment scenario.
Step 3: Assign NLOS propagation condition to the channel between the STX/SRX and each of the 3 RPs. Note: O2I is not applicable.
Step 4: Generate the channel between the STX/SRX and each of the 3 RPs using Step 3 to Step12 of Clause 7.5 and Clause 7.6.10 with parameters derived by Table 7.9.3-3, with updates as follows.
- In Step 7 of Clause 7.5, the arrival angles
- Step 8 of Clause 7.5 is not applicable, except that the random coupling of AOD angles
- The absolute time of arrival in clause 7.6.9 is applied with
NOTE: In the UT monostatic sensing in UMa and UMi scenario, the ZOD offset should be set as 0.
Step 5: Generate the background channel for the STX/SRX
The background channel for the STX/SRX
where,
-
-
Table 7.9.4.2-1: Parameters of background channel for TRP monostatic sensing
Sensing scenarios | UMi | UMa / Urban grid / Highway(FR2) / HST(FR2) | RMa / Highway(FR1) / HST(FR1) | Indoor office | Indoor Factory | |
Distance (m) | αd | 6.1996 | 10.3370 | 6.2025 | 4.236 | 0.039836 |
βd | 0.1558 | 0.1317 | 0.0391 | 0.19255 | 0.179783 | |
cd | 15.2697 | 68.7778 | 1.2940 | 4.99 | 1.130020 | |
Height (m) | αh | 12.0487 | 16.2253 | 0.0007 | 1.3293 | 0.283447 |
βh | 2.3261 | 1.9218 | 5.0146 | 0.1442 | 0.435965 | |
ch | 0.0157 | 2.6142 | 0.0522 | -13.19 | -17.043530 | |
Table 7.9.4.2-2 Part-1: Parameters of background channel for UT monostatic sensing
Sensing scenarios | UMi | UMa / Urban grid / Highway(FR2) / HST(FR2) | RMa / Highway(FR1) / HST(FR1) | Indoor office | Indoor Factory | |
Distance (m) | αd | 10.0220 | 2.9072 | 10.2421 | 4.3733 | 0.231418 |
βd | 1.2522 | 0.1031 | 0.0526 | 0.4457 | 0.128133 | |
cd | 11.0040 | 3.8471 | 3.3131 | 4.6302 | 2.004903 | |
Height (m) | αh | 3.0487 | 1.6640 | 0.3175 | 0.2974 | 0.462968 |
βh | 1.9128 | 1.6215 | 1.4150 | 0.4103 | 0.281526 | |
ch | 0.1785 | -1.4205 | 1.5906 | 2.9711 | -16.921515 | |
Table 7.9.4.2-2 Part-2: Parameters of background channel for UT monostatic sensing
Sensing scenarios | UMi-AV | UMa-AV | RMa-AV | |
Distance (m) | αd | |||
βd | ||||
cd | ||||
Height (m) | αh | |||
βh | ||||
ch | ||||
N OTE: | ||||
7.9.4.3 Combining target channel and background channel #
The channel model for ISAC for a pair of STX and STX is the sum of the target channel(s) and the background channel generated in Clause 7.9.4.1 and 7.9.4.2, i.e.,
7.9.5 Additional modelling components #
7.9.5.0 Introduction #
Similar to Clause 7.6, the additional modelling components in this clause are to support advanced simulations, such as simulations in which spatial consistency is important, simulations that include known type-2 EO, simulations that need adjusting the sum power of the target channel and the background channel, simulations of moving scatterers, simulation with impacts of lower power clusters in the background channels, and simulations of blockage effects of ST.
These extensions are computationally more expensive and might not be required in all evaluation cases.
7.9.5.1 Spatial consistency #
The spatial consistency procedure is used to generate the random variables for the STX-SPST links, the SPST-SRX links and the background channels, so that they are spatial consistent.
For sensing scenario UMi-AV, UMa-AV and RMa-AV, the 2D random process (in the horizontal plane) can be extended to a 3D random process within each applicability range in terms of aerial UE height (defined in 36.777). The correlation distance is equal to the horizontal correlation distance defined in the previous clauses within Clause 7. The random coupling of NLOS rays of Option 2 in Step 9 in Clause 7.9.4.1 shall be kept unchanged per simulation drop even if STX/ST/SRX position(s) changes during simulation.
The spatial consistency procedures in Clause 7.6.3 are reused to handle the links between TRPs and STs/UTs. RSU-type UE is considered as a TRP in the spatial consistency procedures. The spatial consistency procedures in Clause 7.6.3 applies to the links between UT and UT/SPSTs (denoted as UT-UT/SPST links), with a new correlation type for the large scale parameter, cluster specific parameters and ray specific parameters of the links.
- Link-correlated: parameters of any two links of UT-UT/SPST links are correlated, subjected to correlation distance.
- All-correlated: all UT-UT/SPST links are correlated.
In Table 7.9.5.1-1, the correlation type for each parameter of the new spatial consistency procedure is clarified.
Table 7.9.5.1-1: Correlation type among UT-UT/SPST links
Parameters | Correlation type |
Delays | Link-correlated |
Cluster powers | Link-correlated |
AOA/ZOA/AOD/ZOD offset | Link-correlated |
AOA/ZOA/AOD/ZOD sign | Link-correlated |
Random coupling | Link-correlated |
XPR | Link-correlated |
Initial random phase | Link-correlated |
LOS/NLOS states | Link-correlated |
Blockage (Model A) | All-correlated |
O2I penetration loss | All-correlated |
Indoor distance | All-correlated |
Indoor states | All-correlated |
Spatial consistency is not modelled among STX-SPST links, the SPST-SRX links and the background channels in the following situations:
- Different link types, e.g., outdoor LOS, outdoor NLOS or O2I, as defined in Table 7.6.3.4-2.
- UE locates on different floors, as defined in Table 7.6.3.4-2.
- Background channel for monostatic sensing mode, and any link (STX-SPST link, SPST-SRX link, background channel) for bistatic sensing mode.
- Links associated with different non-co-located TRPs, e.g. TRP1-ST/UT/TRP and TRP2-ST/UT/TRP.
- Links that are generated referring to channel models with parameter values of different communication scenarios.
- The background channels for UT monostatic sensing across different UTs or across different RPs for the same UT.
- Between TRP-target/UT link and target/UT-UT link for sensing scenario UMi, InH and InF.
- Between TRP-TRP link and any other links for ISAC channel.
- The background channels for TRP monostatic sensing across different TRPs or across different RPs for the same TRP.
The spatial consistency across the links between STX/SRX and multiple SPSTs of a ST are modelled as if the multiple SPSTs are multiple STs.
7.9.5.2 Type-2 environment object #
A type-2 EO, e.g., wall, is modelled as a surface with finite size. A ray specularly reflected by a type-2 EO can be modelled in the STX-SPST link, SPST-SRX link and optionally in the background channel, if a specular reflection point can be found within the surface of the type-2 EO.
A general procedure to generated small scale parameters of a type-2 EO, e.g., a wall, is provided assuming the incident ray comes from Tx and the scattered ray arrives at Rx.
Step A: Check whether a ray specularly reflected by a type-2 EO needs to be modelled or not for the pair of Tx and Rx
The type-2 EO can be described by a plane equation perpendicular to the ground.
The location of reflection point
where,
If
Step B: Generate parameters of the ray specularly reflected by a type-2 EO if the reflection point is only the surface of the type-2 EO
The 3D distance from the Tx to the reflection point then to the Rx
The ZoD and AoD from Tx to the reflection point are derived based on the location of Tx and reflection point.
The ZoA and AoA from Rx to the reflection point are derived based on the location of Rx and reflection point.
The effective polarization matrix of the type-2 EO reflection ray is given by
where,
-
-
- The formula 7.6-38 and 7.6-39 of reflection coefficients
The reflection coefficients for parallel and perpendicular polarization are given by
When a type-2 EO is deployed in the simulation area, the following modification to the ISAC channel generation in Clause 7.9.4, 7.9.4.1 and 7.9.4.2 can be applied.
1. In Step 1 in Clause 7.9.4.0,
b) Give number of type-2 EO, additionally.
c) Give 3D locations of type-2 EO in the global coordinate system, additionally.
e) Give the orientation of type-2 EO in the global coordinate system, additionally. The type-2 EO orientation is defined by three angles ΩEO,α (type-2 EO bearing angle), ΩEO,β (type-2 EO downtilt angle) and ΩEO,γ (type-2 EO slant angle).
NOTE: In case wrapping is used, each wrapping copy of a type-2 EO should be treated as a separate type-2EO considering channel generation.
2. In Step 2 in Clause 7.9.4.1,
Two options are provided to determine the LOS condition of a STX-SPST link and a SPST-SRX link.
- Option 1: If a type-2 EO blocks the LOS ray of a link, the LOS probability of the link is equal to 0, and otherwise use the LOS probability equation defined in reference TRs in Clause 7.9.3 to determine propagation condition (LOS/NLOS) of the link.
- Option 2: no change to Step 2 in Clause 7.9.4.1.
NOTE: The same option is used to determine the LOS condition of a STX-SPST link, a SPST-SRX link and the background channel.
3. Between Step 8 and 9 in Clause 7.9.4.1, insert one step to generate NLOS rays specularly reflected by type-2 EO, if present.
In a STX-SPST link, the general procedure to model a type-2 EO as described above is executed by substitute Tx with the STX and Rx with the SPST. A NLOS ray specularly reflected by a type-2 EO, if present, is represented by
In a SPST-SRX link, the general procedure to model a type-2 EO as described above is executed by substitute Tx with the SPST and Rx with the SRX. A NLOS ray specularly reflected by a type-2 EO, if present, is represented by
4. In Step 9 in Clause 7.9.4.1,
The following paths are generated and added to set
- A NLOS ray specularly reflected by type-2 EO, if present in the SPST-SRX link is coupled with a NLOS ray specularly reflected by type-2 EO, if present in the STX-SPST link
- A NLOS ray specularly reflected by type-2 EO, if present in the SPST-SRX link is coupled with any LOS ray (if present) and NLOS rays of stochastic clusters in the STX-SPST link
- A NLOS ray specularly reflected by type-2 EO, if present in the STX-SPST link is coupled with any LOS ray (if present) and NLOS rays of stochastic clusters in the SPST-SRX link
5. In Step 10 in Clause 7.9.4.1,
- If the STX-SPST link is in LOS condition,
- If the STX-SPST link is not in LOS condition,
- If the SPST-SRX link is in LOS condition,
- If the SPST-SRX link is not in LOS condition,
6. In Step 11 in Clause 7.9.4.1,
For a NLOS ray specularly reflected by a type-2 EO in the STX-SPST link, if present,
For a NLOS ray specularly reflected by a type-2 EO in the SPST-SRX link, if present,
7. The outcome of Steps 1-12 in Clause 7.9.4.1 shall be identical for all the links from co-sited sectors to a STX/ST/EO/SRX.
8. In Clause 7.9.4.2, if a type-2 EO is modelled in the background channel between a pair of STX and SRX, the general procedure to model a type-2 EO as described above is executed by substitute Tx with the STX and Rx with the SRX.
7.9.5.3 Power normalization across target channel and background channel #
To combine the target channel and the background channel, power normalization can be applied to keep the same/similar channel power as the background channel without sensing target.
7 .9.5.4 Doppler of mobile scatterers #
To support scatterer mobility, the Doppler frequency component
where,
-
-
7.9.5.5 Lower power clusters #
In addition to the clusters/rays in background channel generated in Clause 7.9.4.2, more clusters/rays using NLOS propagation condition can be generated between a sensing Tx and a sensing Rx for bi-static sensing or between a sensing Tx and a reference point r, where r=0, 1, 2 for mono-static sensing to simulate the weak rays in the background channel. The following procedure is applicable for bistatic sensing or for monostatic sensing.
Step 1: Generate a first background channel
Step 2: Generate a second background channel
Step 3: The power of the clusters/rays in the second background channel is scaled down by
7.9.5.6 Blockage #
The blockage models in Clause 7.6.4 can be reused as an optional feature to model the blocking effects in the ISAC channel including the blockage due to the ST(s). The blockage of ST(s) can be applied to the LOS/NLOS rays in the background channel of the ST(s). The blockage of a first ST can be applied to the LOS/NLOS rays in the STX-ST and ST-SRX link of a second ST. The location, orientation and size of the ST as a blocker is known before applying the blockage models.
7. 9.6 Channel model calibration #
7.9.6.1 Large scale calibration #
For the purposes of large scale calibration without fast fading modelling for sensing targets UAV, human, automotive and AGV, the following calibration parameters are respectively provided in Table 7.9.6.1-1/2/3/4. Unspecified parameters in Table 7.9.6.1-2/3/4 are the same as those in Table 7.9.6.1-1. The calibration results based on TR 38.901 V19.0.0 can be found in R1-2506616.
Table 7.9.6.1-1: Simulation assumptions for large scale calibration for UAV sensing targets
Parameters | Values |
Scenario | UMa-AV |
Sensing mode | TRP monostatic, TRP-TRP bistatic, TRP-UE bistatic, UE-UE bistatic |
Target type | UAV of small size (0.3m x 0.4m x 0.2m) |
Sectorization | Single 360-degree sector can be assumed |
Carrier Frequency | FR1: 6 GHz FR2: 30 GHz |
BS antenna configurations | Single dual-pol isotropic antenna |
BS Tx power | FR1: 56dBm FR2: 41dBm |
Bandwidth | FR1: 100MHz FR2: 400MHz |
BS noise figure | FR1: 5dB FR2: 7dB |
UT antenna configurations | Single dual-pol isotropic antenna; (M,N,P,Mg,Ng;Mp,Np) = (1,1,2,1,1;1,1) |
UT noise figure | FR1: 9dB FR2: 10dB |
UT height | 1.5m for terrestrial UTs, |
UT Tx power | 23dBm |
UT Distribution | - The overall number of UTs is 30 uniformly distributed in the center cell. - All of the UTs are either terrestrial UTs or aerial UTs, all outdoors. - Vertical distribution of aerial UE: Fixed height value of 200 m. - FR1 is assumed for aerial UE. |
Sensing target distribution | 1 target uniformly distributed (across multiple drops) within the center cell. Vertical distribution: Fixed height value of 200 m. |
Component | -12.81 dBsm |
Minimum 3D distances between STX/SRX and sensing target | 10 m |
Wrapping Method | No wrapping method is used if interference is not modelled, otherwise geographical distance based wrapping |
Coupling loss for target channel | power scaling factor (pathloss, shadow fading, and RCS component |
STX/SRX selection | Best N = 4 STX-SRX pairs to be selected for the target.
see note 1 |
Metrics | Coupling loss for target channel Coupling loss for background channel (in case of monostatic sensing, this is the coupling loss between Tx and one reference point) see note 2 |
NOTE 1: Based on the STX-SRX pairs with the smallest power scaling factor of the target channel. NOTE 2: CDFs can be separately generated for target channel, background channel. | |
Table 7.9.6.1-2. Simulation assumptions for large scale calibration for Human sensing targets
Parameters | Indoor Values | Outdoor Values |
Scenario | Indoor office 12 sectors per 120m * 50m * 3m ISD = 20m InF-SH Hall size: L=300 x W=150 m Room height: 10m 18 BSs on a square lattice with spacing D, located D/2 from the walls. - D=50m
| UMa (ISD = 500m), UMi (ISD= 200m) |
Sensing mode | TRP monostatic, TRP-TRP bistatic, TRP-UE bistatic, UE-UE bistatic, UE monostatic | |
Target type | Adult Pedestrian: 0.5m x 0.5m x 1.75m. See note. | |
BS Tx power | FR1: 24dBm FR2: 23dBm | FR1: 56dBm FR2: 41dBm |
UT height | 1m | 1.5m |
UT Distribution | Per Table 7.8-1 Indoor-Office Number of UTs: 20 | Per Table 7.8-1. Number of UTs/cell: 10 |
Sensing target distribution | 100% indoor, 1 target uniformly distributed (across multiple drops) over the convex hull of the TRP deployment | 100% outdoor. 1 target uniformly distributed (across multiple drops) within the center cell. |
Component | -1.37 dBsm
| |
Minimum 3D distances between STX/SRX and sensing target | For TRP monostatic and TRP-TRP bistatic sensing modes, the minimum 2D distance between STX/SRX and the sensing target is 0 m For UE monostatic and UE-UE bistatic sensing modes, the minimum 2D distance between STX/SRX and the sensing target is 1 m (as specified in TR 38.858) | For TRP monostatic and TRP-TRP bistatic sensing modes, the minimum 2D distances between STX/SRX and the sensing target are 10 m for UMi scenarios and 35 m for UMa scenarios For UE monostatic and UE-UE bistatic sensing modes, the minimum 2D distance between STX/SRX and the sensing target is 1 m (as specified in TR 38.858) |
Wrapping Method | N/A | No wrapping method is used if interference is not modelled, otherwise geographical distance based wrapping |
NOTE: Height of scattering point 1.5m | ||
Table 7.9.6.1-3. Simulation assumptions for large scale calibration for Automotive sensing targets
Parameters | Values |
Scenario | For FR1: - Urban Grid (ISD=500m, BS height=25m) - Highway (ISD=1732m, BS height=35m) For FR2: - Urban Grid (ISD=250m, BS height=25m) - Highway (ISD=500m, BS height=25m)
For Urban Grid ISD =250m - The layout is configured as follows: - Red triangles: BS with 250m ISD, 18 BSs are located.
|
Sensing mode | TRP monostatic, TRP-TRP bistatic, TRP-UE bistatic, UE-UE bistatic, UE monostatic |
Target type | Vehicle type 2 [TR37.885] |
UT height | 1.5m for pedestrian type UE 5m for RSU type UE 1.6m for vehicle type UE |
UT Distribution | For Highway: - Vehicle Type UT distribution follows vehicle UE dropping as in Clause 6.1.2 from TR37.885. - RSU-type UTs are uniformly allocated with 100m spacing in the middle of the freeway as per TR37.885. For Urban grid: - Vehicle Type UT distribution follows vehicle UE dropping as in Clause 6.1.2 from TR37.885. - Pedestrian type UT, the dropping using equally spaced along the sidewalk with a fixed inter-pedestrian X m dropped per TR37.885 - Total number of pedestrian UEs is 16 in the centre grid. - Pedestrian UE is in the middle of the sidewalk - The inter-pedestrian UE distance (m) (i.e., X) is calculated by ‘A/(16*N)’, where ‘A’ is the total length of sidewalk where the pedestrian UEs are dropped under the assumption of ‘N’ road grids (i.e., ‘{(250m – 17m) + (433m – 17m)} * 2 * N’). - N=1; - RSU-type UT: the dropping is at the center of intersection per TR37.885. see note 1 |
Sensing target distribution | Per TR37.885:-<br> Option A-<br> Vehicle type distribution: 100% vehicle type 2.-<br> Clustered dropping is not used. - Highway: one target uniformly distributed (across multiple drops) within the simulation region. Vehicle speed is 140 km/h in all the lanes as baseline. - Urban Grid: one target is uniformly distributed (across multiple drops) within the center road grid. Vehicle speed is 60 km/h in all the lanes as baseline.
see note 2 |
Component | 11.25 dBsm
|
Minimum 3D distances between STX/SRX and sensing target | Min distances based on min TRP/UE distances defined in TR37.885 |
Wrapping Method | No wrapping method is used if interference is not modelled, otherwise geographical distance based wrapping. As defined in urban grid/highway scenario |
STX/SRX selection | Best N = 4 STX-SRX pairs to be selected for the target, with the following exceptions for Highway for FR1: - For TRP-TRP monostatic sensing, N=2 - For TRP-TRP bistatic sensing, N=1 |
NOTE 1: A single UT type is used per calibration, e.g., pedestrian type UT, RSU type UT, or vehicle type UTPer TR37.885 NOTE 2: A vehicle with size L*W*H can be dropped with 5 scattering points (front/left/right/back/roof), respectively with locations (L/2,0,H/2), (0,W/2,H/2), (0,-W/2,H/2), (-L/2,0,H/2) and (0,0,H) in LCS. A vehicle with size L*W*H can be dropped with 1 scattering point with location (0,0,H/2) in LCS. In the case of vehicle with 5 scattering points, spatial consistency is enabled with the following assumptions: - The correlation for LOS/NLOS condition of the 5 points is assumed equal to 1. LOS/NLOS condition can be calculated based on the distance of the STX/SRX to the centroid of the ST, then apply the LOS/NLOS condition to each of the 5 points. - The correlation for stochastic cluster paths of the 5 points is assumed equal to 1. The stochastic cluster paths can be calculated between the STX/SRX and the centroid of the ST, then the stochastic cluster paths are added to each of the 5 points. | |
Table 7.9.6.1-4. Simulation assumptions for large scale calibration for AGV sensing targets
Parameters | Values |
Scenario | InF: 18 TRPs per the big hall: L = 300 m x W = 150 m
- X-axis is pointing down to the floor - The antenna array is mounted in the Y-Z plane with boresight along the X-axis - The X-axis/Y-axis/Z-axis refer to LCS - 8 m for high BS scenario |
Sensing mode | TRP monostatic, TRP-TRP bistatic, TRP-UE bistatic, UE-UE bistatic, UE monostatic |
Target type | Option 1: 0.5m x 1.0m x 0.5m |
BS Tx power | FR1: 24dBm FR2: 23dBm |
UT height | 1.5m |
UT Distribution | Per Table 7.8-7 Indoor Factory. Number of UTs: 30 |
Sensing target distribution | 100% indoor, 1 target uniformly distributed (across multiple drops) over the convex hull of the TRP deployment |
Component | -4.25 dBsm see note |
NOTE: For calibration purposes, other value(s) are not precluded, | |
7.9.6.2 Full calibration #
For the purpose of full calibration including the fast fading modelling for sensing scenarios UAV, human, automotive and AGV, the calibration parameters are respectively provided in Table 7.9.6.2-1/2/3/4. Unspecified parameters in the tables are the same as those in Table 7.9.6.1-1/2/3/4 for the same sensing target. If still not specified, the parameters in Table 7.9.6.2-1 are used. The calibration results based on TR 38.900 V19.0.0 can be found in R1-2506616.
Table 7.9.6.2-1: Simulation assumptions for full calibration for UAV sensing targets
Parameters | Values |
Scenario | UMa-AV |
Sensing mode | TRP monostatic, TRP-TRP bistatic, TRP-UE bistatic, UE-UE bistatic |
Target type | UAV of small size (0.3m x 0.4m x 0.2m) |
RCS for each scattering point | Component Component Component The same values are used for monostatic RCS and bistatic RCS |
Fast fading model | TR 36.777 Annex B.1.3 |
(u, std) for XPR of target | Mean 13.75 dB, deviation 7.07 dB |
The power threshold for path dropping after concatenation for target channel | -40 dB |
Coupling loss for target channel | By definition, need to consider all direct and indirect paths. The following parameters are included in the calculation: - power scaling factor (pathloss, shadow fading, and RCS component - for small scale RCS |
| |
Absolute delay | The model of UMa scenario defined in Table 7.6.9-1 is reused for UMa-AV for all sensing modes. |
Metrics | Coupling loss for target channel Coupling loss for background channel (in case of monostatic sensing, this is the linear sum of coupling losses between STX/SRX and all reference points) See note.
CDF of Delay Spread and Angle Spread (ASD, ZSD, ASA, ZSA). For monostatic sensing mode: delay spread and angle spread of the background channel is calculated separately for each reference point. Definition of Delay Spread is similar to the definition of angle spread in Annex A of TR 25.996, Definition of Angle Spread can ref to Annex A of TR 25.996. |
NOTE: CDFs can be separately generated for target channel, background channel | |
Table 7.9.6.2-2: Simulation assumptions for full calibration for Human sensing targets
Parameters | Indoor Values | Outdoor Values |
Scenario | As specified in Table 7.9.6.1-2 | As specified in Table 7.9.6.1-2 |
Sensing mode | TRP monostatic, TRP-TRP bistatic, TRP-UE bistatic, UE-UE bistatic, UE monostatic | |
Target type | Adult Pedestrian: 0.5m x 0.5m x 1.75m see note | |
UT Distribution | Per Table 7.8-2 Indoor-Office Number of UTs: 20 | Per Table 7.8-2 Number of UTs/cell: 10 |
RCS for each scattering point | Component Component Component The same values are used for monostatic RCS and bistatic RCS | Component Component Component The same values are used for monostatic RCS and bistatic RCS |
Fast fading model | For BS to UE link: Follow the procedure defined in TR38.901For BS to target link: Follow the procedure defined in TR38.901<br>for target to UE link: Follow the procedure defined in 38.858<br> | For BS to UE link: Follow the procedure defined in TR38.901For BS to target link: Follow the procedure defined in TR38.901<br>for target to UE link: Follow the procedure defined in 38.858<br> |
(u, std) for XPR of target | (19.81, 4.25) dB | (19.81, 4.25) dB |
Absolute delay | The model of InH, InF-SH scenarios defined in Table 7.6.9-1. | The model of UMa/UMi scenario defined in Table 7.6.9-1. |
NOTE: Height of scattering point 1.5m | ||
Table 7.9.6.2-3: Simulation assumptions for full calibration for Automotive sensing targets
Parameters | Values |
Scenario | As specified in Table 7.9.6.1-3 |
Sensing mode | TRP monostatic, TRP-TRP bistatic, TRP-UE bistatic, UE-UE bistatic, UE monostatic |
Target type | Vehicle type 2 [TR37.885] |
RCS for each scattering point | Based on the RCS model in Clause 7.9.2.1. |
Fast fading model (full calibration) | Procedures based on 37.885 |
(u, std) for XPR of target | (21.12, 6.88) dB. |
Absolute delay | The model of UMa scenario defined in Table 7.6.9-1 is reused for Highway (FR2)/Urban Grid for all sensing modes. The model of RMa scenario defined in Table 7.6.9-1 is reused for Highway (1732m ISD) for all sensing modes. |
Table 7.9.6.2-4: Simulation assumptions for full calibration for AGV sensing targets
Parameters | Values |
Scenario | As specified in Table 7.9.6.1-4 |
Sensing mode | TRP monostatic, TRP-TRP bistatic, TRP-UE bistatic, UE-UE bistatic, UE monostatic |
Target type | Option 1: 0.5m x 1.0m x 0.5m |
RCS for each scattering point | The values/pattern of component The same values are used for monostatic RCS and bistatic RCS |
Fast fading model (Full calibration) | For BS to UE link: Follow the procedure defined in TR38.901 For BS to target link: Follow the procedure defined in TR38.901 for target to UE link: Follow the procedure defined in TR 38.858 |
(u, std) for XPR of target | (9.60, 6.85) dB |
Absolute delay | The model of InF scenario defined in Table 7.6.9-1 is reused for all sensing modes. |
7.9.6.3 Calibration of additional features #
The calibration parameters for the calibration of spatial consistency and type-2 EO can be respectively found in Table 7.9.6.3-1/2. Unspecified parameters in these tables are the same as those in Tables 7.9.6.1-2/3 and Tables 7.9.6.2-2/3. The calibration results based on TR 38.900 V19.0.0 can be found in R1-2506616.
Table 7.9.6.3-1: Simulation assumptions for calibration of spatial consistency
Parameter | Values |
Scenario | Urban grid, Indoor office |
Cell layout | Urban grid: ISD = 500m, the same layout with 37.885 Indoor office: Office size (WxLxH): 120mx50mx3m, ISD=20m (refer to TR 38.901) |
Sensing mode | TRP- pedestrian UE bistatic in urban grid and TRP-UE bistatic in indoor office |
Polarized antenna model | Model-2 in clause 7.3.2 in TR 38.901 |
Scattering point | Single point |
Calibration link and calibration method | - Calibration link: same target associated to different UT as following figure
- Calibration method Step1: In single drop simulation, drop one Target and multiple UTs. Step2: Generate the channel of each link based on Target level spatial consistency method. Step3: Determine Target-UT groups based on the distance between UTs. Step4: Bin all the link pairs into certain distance groups, e.g., - 0m <= the location distance of link pair < 1m -> 0m group - 1m <= the location distance of link pair < 2m -> 1m group - 2m <= the location distance of link pair < 3m -> 2m group - 3m <= the location distance of link pair < 4m -> 3m group … - (N)m <= the location distance of pair < (N+1)m -> (N)m group (see note 2) Step5: Calculate the correlation coefficient metric of each distance groups. Step6: Draw the x-y cure, where x=0, …, N, y= correlation coefficient metric related to (x)m group |
ST and UT distribution | - Urban grid one target is uniformly dropped within the center grid in the outside lane with length of 433m, the UT is distributed by a uniform distribution distance of [0,1]m in the walk road where is in the same street with the target. As following figure.
see note 3
- Indoor office 1 Target is uniform dropped in office region 10 UTs are dropped uniformly per cell, i.e., totally 120 UTs are uniformly dropped in office region. |
Metrics | Cross-correlation coefficient of delay for the third cluster of channel between link pairs. Cross-correlation coefficient of AOA (for UT) for the third cluster of channel between link pairs. Cross-correlation coefficient of LOS/NLOS status of channel between link pairs(only for indoor office scenario). |
NOTE 1: Other necessary but unspecified parameters in this table are the same as those in the table for urban grid scenario calibration and indoor office scenario calibration. NOTE 2: N is at least twice the maximum correlation distance associated with the channel parameters in the scenario NOTE 3: The ST-UT link only consider LOS condition discarding NLOSv condition. | |
Table 7.9.6.3-2: Simulation assumptions for calibration of type-2 EO
Parameter | Values |
Scenario | Urban grid |
Cell layout | For FR1: ISD = 500m, the layout is defined as below: The BSs are placed at the top of buildings at one corner. Specifically, the road grids shall be shifted by (
For FR2 ISD=250m, the cell layout is as that specified in Table 7.9.6.1-3 |
Sensing mode | TRP- pedestrian UE bistatic TRP- RSU type UE bistatic TRP monostatic TRP-TRP bistatic RSU type UE - RSU type UE bistatic (optional) |
EO deployment | 9 buildings with size 413m x 230m x 20m illustrated by the green blocks as in the figure shown in the row of cell layout. |
ST distribution | one target is uniformly distributed (across multiple drops) within the center of the outside lane of the grid. |
UT distribution | - For pedestrian UT - Pedestrian type UE, the dropping using equally spaced along the sidewalk with a fixed inter-pedestrian X m dropped per TR37.885. - Total number of pedestrian UEs is 16 in the centre grid. - Pedestrian UE is in the middle of the sidewalk - The inter-pedestrian UE distance (m) (i.e., X) is calculated by ‘A/(16*N)’, where ‘A’ is the total length of sidewalk where the pedestrian UEs are dropped under the assumption of ‘N’ road grids (i.e., ‘{(250m – 17m) + (433m – 17m)} * 2 * N’). - N=1; - For RSU type UT - The dropping is at the center of intersection per TR37.885. |
EM-parameters | Refer to the row for concrete in Table 7.6.8-1 |
Metrics | CDF curves: - Full coupling loss: calculate the coupling loss for each Tx-EO-ST-LOS-Rx and Tx-LOS-ST-EO-Rx rays. - CDF of the Delay. - CDF of the AoA, AoD, ZoA, ZoD. Additional CDF curves: - Coupling loss for target channel - CDF of Delay Spread and Angle Spread (ASD, ZSD, ASA, ZSA). Definition of Delay Spread is similar to the definition of angle spread [TR 25.996, Annex A]. |
NOTE: Other necessary but unspecified parameters in this table are the same as those in the table for urban grid scenario calibration. | |
8 Map-based hybrid channel model (Alternative channel model methodology) #
Map-based hybrid model is composed of a deterministic component following, e.g., METIS work [6] and a stochastic component following mainly the model described in clause 7. The channel model methodology described in this clause is an alternative to the methodology specified in clause 7, and can be used if:
- The system performance is desired to be evaluated or predicted with the use of digital map to take into account the impacts from environmental structures and materials.
The map-based hybrid model defined in this clause is not calibrated and can be used per company basis.
8.1 Coordinate system #
The same coordinate system as defined in Clause 7.1 is applied.
8.2 Scenarios #
The same scenarios as in Clause 7.2 can be applied.
8.3 Antenna modelling #
The same antenna modelling as defined in Clause 7.3 can be applied.
8.4 Channel generation #
The radio channels are created using the deterministic ray-tracing upon a digitized map and emulating certain stochastic components according to the statistic parameters listed in Tables 7.5-6 to 7.5-11 [Note: Not all parameters listed in these tables are used in hybrid model]. The channel realizations are obtained by a step-wise procedure illustrated in Figure 8.4-1 and described below. In the following steps, downlink is assumed. For uplink, arrival and departure parameters have to be swapped.
Figure 8.4-1: Channel coefficient generation procedure
Step-wise procedure:
Step 1: Set environment and import digitized map accordingly
a) Choose scenario. Choose a global coordinate system and define zenith angle θ, azimuth angle ϕ, and spherical basis vectors , as shown in Figure 7.5-2.
b) Import digitized map according to the chosen scenario. The digitized map should at least contain the following information:
- The 3D geometric information for each of major structures involving with buildings or rooms. The external building walls and internal room walls are represented by surfaces and identified by the coordinates of the vertices on each wall.
- The material and thickness of each wall as well as the corresponding electromagnetic properties including permittivity and conductivity.
- Random small objects in certain scenarios (e.g, UMi outdoor)
The format of digitized map, including additional information besides above-mentioned, is per implementation wise and out of scope of this description.
Step 2: Set network layout, and antenna array parameters
a) Give number of BS and UT.
b) Give 3D locations of BS and UT, and calculate LOS AOD (ϕLOS,AOD), LOS ZOD (θLOS,ZOD), LOS AOA (ϕLOS,AOA), LOS ZOA (θLOS,ZOA) of each BS and UT in the global coordinate system
c) Give BS and UT antenna field patterns Frx and Ftx in the global coordinate system and array geometries
d) Give BS and UT orientations with respect to the global coordinate system. BS array orientation is defined by three angles ΩBS,α (BS bearing angle), ΩBS,β (BS downtilt angle) and ΩBS,γ (BS slant angle). UT orientation is defined by three angles ΩUT,α (UT bearing angle), ΩUT,β (UT downtilt angle) and ΩUT,γ (UT slant angle). Give rotational motion of UT in terms of its bearing angle, downtilt angle and slant angle if UT rotation is modelled.
e) Give speed and direction of motion of UT in the global coordinate system for virtual motion.
f) Give system centre frequency/frequencies and bandwidth(s) for each of BS-UT links
If the bandwidth (denoted as 
) is greater than c/D Hz, where c is the speed of light and D is the maximum antenna aperture in either azimuth or elevation, the whole bandwidth is split into 
equal-sized frequency bins, where 
is a per-implementation parameter taking into account the channel constancy as well as other potential evaluation needs, and the bandwidth of each frequency bin is 
. Within k-th frequency bin, the channel power attenuation, phase rotation, Doppler are assumed constant, whose corresponding values are calculated based on the centre frequency of k-th frequency bin
for 
, where 
is the centre frequency of the corresponding BS-UT link.
Step 3: Apply ray-tracing to each pair of link ends (i.e., end-to-end propagation between pair of Tx/Rx arrays).
a) Perform geometric calculations in ray-tracing to identify propagation interaction types, including LOS, reflections, diffractions, penetrations and scattering (in case the digitized map contains random small objects), for each propagation path. In general, some maximum orders of different interaction types can be set.
- The theoretical principles and procedures of geometric tracing calculations can be found in [6]~[10][12]. This description does not intend to mandate new concepts and/or procedures to the conventional ray-tracing algorithms; on the other hand, the implementation-based variations aiming to reduce computation complexity are allowed within limits of acceptable calibration tolerances.
- The same geometric calculation is shared among all 
frequency bins.
b) Perform electric field calculations over propagation path, based on identified propagation interaction types (LOS, reflection, diffraction, penetration and scattering) and centre frequencies of frequency bins.
The details of electric field calculation can be found in [6]~[13].
The modelling algorithms in geometry and electric field calculations for different propagation interactions are summarized in the table below.
Table 8.4-1 Principles applied in ray-tracing
| Geometry calculation | Electric field calculation |
LOS | Free space LOS | Friis equation [11] |
Reflection | Snell's law with image-based method [7] | Fresnel equation [7] |
Diffraction | Fermat's principle [12] | UTD [13] |
Penetration | Snell's law for transmission through slab [8] | Fresnel equation [7] |
Scattering (upon small objects) | Isotropic scattering [6] | RCS-based scattering coefficient [6] |
Note: For reasons of simplicity and simulation speed, the maximum order of reflection on a path without diffraction is configurable from {1,2,3}; the maximum order of diffraction on a path without reflection is configurable from {1,2}; the path containing both reflection and diffraction has 1-order reflection and 1-order diffraction, besides any potential penetrations; and the maximum order of penetration on a path is configurable, with the recommended value equal to 5.
The outputs from Step 3 should at least contain following for each pair of link ends:
- the LOS/NLOS flag to indicate whether a LOS propagation mechanism exists;
- the number of deterministic propagation paths 
(also referred as deterministic clusters in Step 8. To avoid the unnecessary computation complexity, these deterministic paths only include those paths whose powers are higher than 25dB below the maximum deterministic path power, where the path power is denoted as 
and defined below);
- for each deterministic path (
-th path sorted in ascending order of path delay):
- the flag indicating whether the deterministic path is generated with scattering upon random small objects;
- the normalized path delay 
and the first arrival absolute delay
(with 
to be the real absolute propagation delay of the path);
- angles of arrival and departure [
,
,
,
];
- the power 
for k-th frequency bin, and the path power 
- the XPR 
of the path, where 
with 
being the XPR for k-th frequency bin.
- to support for true motion, i.e. the case when a trajectory is specified for UT, a path ID is associated for each deterministic path. The same ID is associated for a path across a number of UT locations as far as 1) it has same interaction types in the same order and 2) its interactions occur in same walls or other surfaces.
The deterministic paths are sorted by normalized path delay (
) in ascending order. That is to say, 
= 0.
If =0 for a pair of link ends, the channel gain for this pair of link ends is assumed to be zero and the remaining steps are skipped with none of random cluster.
Step 4 : Generate large scale parameters e.g. delay spread, angular spreads and Ricean K factor for random clusters.
The generation of large scale parameters takes into account cross correlation according to Table 7.5-6 and uses the procedure described in Clause 3.3.1 of [14] with the square root matrixbeing generated using the Cholesky decomposition and the following order of the large scale parameter vector: sM = [sK, sDS, sASD, sASA, sZSD, sZSA]T. Limit random RMS azimuth arrival and azimuth departure spread values to 104 degrees, i.e., ASA= min(ASA ,104), ASD = min(ASD ,104). Limit random RMS zenith arrival and zenith departure spread values to 52 degrees, i.e., ZSA = min(ZSA,52), ZSD = min(ZSD,52). For the parameter selection from Table 7.5-6, the LOS/NLOS condition determined in Step 3 is applied.
Step 5: Generate delays (denoted as {
) for random clusters
Delays are drawn randomly according to the exponential delay distribution
(8.4-1)
where 
, Xn ~ uniform(0,1), and cluster index n = 0,…, 
with 
to be configurable. A recommended value for is the number of clusters given in Table 7.5-6.
, where rτ is the delay distribution proportionality factor given in Table 7.5-6.
Normalise the delays by subtracting the minimum delay and sort the normalised delays to ascending order:
(8.4-2)
where 
is the additional scaling of delays to compensate for the effect of LOS peak addition to the delay spread, and is depending on the heuristically determined Ricean K-factor [dB] as generated in Step 4:
(8.4-3)
For the delay used in cluster power generation in Step 6, the scaling factor is always 1.
The n-th random cluster is removed if n=0 or 
for any of 1≤lRT≤LRT, where τth is given by 
, and 
is the configurable probability for cluster inter-arrival interval to be less than τth. For example, set =0.2 to obtain τth=0.223
.
Denote 
for 1≤n≤LRC as the delays of the LRC random clusters that remain after the cluster removal.
As an add-on feature, the absolute time of arrival of a cluster is derived as:
- , if the cluster is the -th deterministic path obtained in Step 3 for 1≤lRT≤LRT;
- +, if the cluster is the n-th random cluster for 1≤n≤LRC.
Step 6: Generate powers (denoted as 
for 1≤i≤LRC) for random clusters.
Cluster powers for the random clusters are calculated assuming a single slope exponential power delay profile. First, the virtual powers (denoted as 
for 1≤i≤LRC) of random clusters and virtual powers (denoted as 
for 1≤j≤LRT) of deterministic clusters are calculated as following.
Denote:
(8.4-4)
(8.4-5)
where i,RC and j,RT are the per cluster shadowing terms in [dB] and meet distribution of N(0,ζ). Then,
(8.4-6)
(8.4-7)
In the case of LOS condition, A=KR with KR being the Ricean K-factor obtained in Step 4 and converted to linear scale; otherwise, A=0. The real power (including effects of pathloss) per random cluster in k-th frequency bin is given by
(8.4-8)
for 1≤i≤LRC and 
. Similar to path power of deterministic cluster, the path power of i-th random cluster is calculated as
. (8.4-9)
Step 7: Generate arrival angles and departure angles for both azimuth and elevation, for each random cluster.
For azimuth angles of the n-th random cluster:
The composite PAS in azimuth of all random clusters is modelled as wrapped Gaussian (see Table 7.5-6). The AOAs are determined by applying the inverse Gaussian function with input parameters 
and RMS angle spread ASA
(8.4-10)
with constant 
defined as
(8.4-11)
where 
is defined as a scaling factor related to the total number of clusters and is given in Table 7.5-2.
In the LOS case, constant also depends on the Ricean K-factor K in [dB], as generated in Step 4. Additional scaling of the angles is required to compensate for the effect of LOS peak addition to the angle spread.
Assign positive or negative sign to the angles by multiplying with a random variable Xn with uniform distribution to the discrete set of {1,–1}, and add component 
to introduce random variation
(8.4-12)
where 
is calculated as
(8.4-13)
Note that 
shall be given in radians here.
The generation of AOD (φn,AOD) follows a procedure similar to AOA as described above.
For zenith angles of the n-th random cluster:
The generation of ZOA assumes that the composite PAS in the zenith dimension of all random clusters is Laplacian (see Table 7.5-6). The ZOAs are determined by applying the inverse Laplacian function with input parameters and RMS angle spread ZSA
(8.4-14)
with 
defined as
, (8.4-15)
where 
is a scaling factor related to the total number of clusters and is given in Table 7.5-4.
In the LOS case, constant 
also depends on the Ricean K-factor K in [dB], as generated in Step 4. Additional scaling of the angles is required to compensate for the effect of LOS peak addition to the angle spread.
Assign positive or negative sign to the angles by multiplying with a random variable Xn with uniform distribution to the discrete set of {1,–1}, and add component 
to introduce random variation
(8.4-16)
where if the UT is located indoors and 
if the UT is located outdoors, where 
is calculated as
(8.4-17)
Note that 
shall be given in radians here.
The generation of ZOD follows the same procedure as ZOA described above except equation (8.4-16) is replaced by
(8.4-18)
where variable Xn is with uniform distribution to the discrete set of {1,–1}, 
, is given in Tables 7.5-7 to 7.5-11.
Step 8: Merge deterministic clusters and random clusters.
First, remove any deterministic or random cluster with less than -25 dB power compared to max{
,} for all 1≤j≤LRT and 1≤i≤LRC. Then, simply put the remaining deterministic clusters and random clusters into single set of clusters, and meanwhile maintain an attribute for each cluster to indicate whether the cluster is a deterministic cluster or a random cluster.
Step 9: Generate ray delays and ray angle offsets inside each cluster, where the cluster can be either random or deterministic.
Denote M as the number of rays per cluster, where M=1 if the cluster corresponds to n=1 in the LOS case, otherwise the value of M is given in Table 7.5-6.
When 
:
The relative delay of m-th ray within n-th cluster is given by 
for m = 1,…,M.
The azimuth angle of arrival (AOA) for the m-th ray in n-th cluster is given by
(8.4-19)
where 
is the cluster-wise rms azimuth spread of arrival angles (cluster ASA) in Table 7.5-6, and offset angle αm is given in Table 7.5-3. 
equals to the AOA angle output from Step 3 if n-th cluster is deterministic cluster, and equals to the AOA angle (8.4-12) in Step 7 if n-th cluster is random cluster.
The generation of AOD (φn,m,AOD) follows a procedure similar to AOA as described above.
The zenith angle of arrival (ZOA) for the m-th ray in n-th cluster is given by
(8.4-20)
where 
is the cluster-wise rms spread of ZOA (cluster ZOA) in Table 7.5-6, and offset angle αm is given in Table 7.5-3. Assuming that is wrapped within [0, 360°], if , thenis set to . 
equals to the ZOA angle output from Step 3 if n-th cluster is deterministic cluster, and equals to the ZOA angle (8.4-16) in Step 7 if n-th cluster is random cluster.
The zenith angle of departure (ZOD) for the m-th ray in n-th cluster is given by
(8.4-21)
where 
is the mean of the ZSD log-normal distribution. 
equals to the ZOD angle output from Step 3 if n-th cluster is deterministic cluster, and equals to the ZOD angle (8.4-18) in Step 7 if n-th cluster is random cluster.
When 
:
The relative delay of m-th ray within n-th cluster is given by 
that are sorted in ascending order, where 
, with the cluster delay spread as given in Table 7.5-6. 
denotes the continuous uniform distribution on the interval 
. Note that 
shall be the independently generated.
The azimuth angles (AOA and AOD) and zenith angles (ZOA and ZOD) for the m-th ray in n-th cluster in each frequency bin is given by
(8.4-22)
for m = 1,…,M, where 
and 
equal to the {AOA,AOD} and {ZOA, ZOD} angle outputs from Step 3 if n-th cluster is deterministic cluster, and equal to the {AOA,AOD} and {ZOA, ZOD} angle in Step 7 if n-th cluster is random cluster; and
(8.4-23)
with the respective cluster angular spreads as given in Tables 7.5-6 to 7.5-11.
Assuming that is wrapped within [0, 360°], if , thenis set to .
Step 10: Generate power of rays in each cluster, where coupling of rays within a cluster for both azimuth and elevation could be needed.
Given 
as the real power in k-th frequency bin for the n-th cluster (either deterministic or random) obtained from Step 8,
When 
:
Couple randomly AOD angles φn,m,AOD to AOA angles φn,m,AOA within a cluster n. Couple randomly ZOD angles with ZOA angles using the same procedure. Couple randomly AOD angles φn,m,AOD with ZOD angles within a cluster n.
The power of m-th ray in n-th cluster and in k-th frequency bin is given by 
for m = 1,…,M.
When :
The power of m-th ray in n-th cluster and in k-th frequency bin is given by 
for m = 1,…,M, where
(8.4-24)
and , , , and are respectively the intra-cluster delay spread and the corresponding intra-cluster angular spreads that are given in Table 7.5-6. The cluster zenith spread of departure is given by
, (8.4-25)
with 
being defined in Tables 7.5-7, 7.5-8, 7.5-9, 7.5-10 and 7.5-11.
Step 11: Generate XPRs
Generate the cross polarization power ratios (XPR) κfor each ray m of each cluster n. XPR is log-Normal distributed. Draw XPR values as
(8.4-26)
where 
is Gaussian distributed with given from Table 7.5-6. If n-th cluster is a deterministic cluster, 
; otherwise, 
is given in Table 7.5-6.
Note: is independently drawn for each ray and each cluster.
Step 12: Draw initial random phases
Draw random initial phase for each ray m of each cluster n and for four different polarisation combinations (θθ, θϕ, ϕθ, ϕϕ). The distribution for initial phases is uniform within (-ππ).
In the LOS case, calculate an initial phase 
for both θθ and ϕϕ polarisations, where d3D is the 3D distance between transmitter and receiver and λ0=c/fc is the wavelength of the modelled propagation link.
Step 13: Generate channel coefficients for each cluster n and each receiver and transmitter element pair u, s.
In case of NLOS, the channel coefficients of ray m in cluster n for a link between Rx antenna u and Tx antenna s at time t in k-th frequency bin can be calculated as
(8.4-27)
where Frx,u,θ and Frx,u,ϕ are the receive antenna element u field patterns in the direction of the spherical basis vectors, 
and 
respectively, Ftx,s,θ and Ftx,s,ϕ are the transmit antenna element s field patterns in the direction of the spherical basis vectors, and 
respectively. The delay (TOA) for ray m in cluster n for a link between Rx antenna u and Tx antenna s is given by:
(8.4-28)
For the m-th ray within n-th cluster, 
is the spherical unit vector with azimuth arrival angle 
and elevation arrival angle 
, given by
(8.4-29)
is the spherical unit vector with azimuth departure angle 
and elevation departure angle 
, given by
(8.4-30)
Also, 
is the location vector of receive antenna element u and 
is the location vector of transmit antenna element s, κn,m is the cross polarisation power ratio in linear scale. If polarisation is not considered, the 2x2 polarisation matrix can be replaced by the scalar 
and only vertically polarised field patterns are applied.
The Doppler frequency component is calculated from the arrival angles (AOA, ZOA), and the UT velocity vector 
with speed v, travel azimuth angle ϕv, elevation angle θv and is given by
, (8.4-31)
In case of LOS, the channel coefficient is calculated in the same way as in (8.4-27) except for n=1:
(8.4-32)
where the corresponding delay (TOA) for cluster n=1 for a link between Rx antenna u and Tx antenna s is given by 
.
In (8.4-27) and (8.4-32), the oxygen absorption loss, OLn,m(f), for each ray m in cluster n at carrier frequency f is modelled as
OLn,m(f) = α(f)/1000 · c · 
[dB] (8.4-33)
where:
- α(f) is the frequency dependent oxygen loss per distance (dB/km) characterized in Clause 7.6.1;
- c is speed of light (m/s); and
- 
is the delay (s) obtained from Step 3 for deterministic clusters and from Step 5 for random clusters. is from the output of Step 3.
In (8.4-27) and (8.4-32), blockage modelling is an add-on feature. If the blockage model is applied, the blockage loss, BLn,m(f,t) in unit of dB, for each ray m in cluster n at carrier frequency f and time t is modelled in the same way as given in Clause 7.6.4; otherwise BLn,m(f,t)=0dB for all f and t.
<br>Annex A: Further parameter definitions<br> #
A.1 Calculation of angular spread #
Based on the circular standard deviation in directional statistics, the following expression for the angular spread AS in radians is proposed
where
A.2 Calculation of mean angle #
The power weighted mean angle is given by
where
A.3 Calculation of cluster angular spread #
The cluster angular spread in degrees is given by
where
A.4 Calculation of cluster mean angle #
The power weighted cluster mean angle in degrees is given by
where
A.5 Calculation of scaling factor for changing CDL model angular spread #
The following expression for the computing scaling factor,
where
<br>Annex B: Change history #
Change history | |||||||
Date | Meeting | TDoc | CR | Rev | Cat | Subject/Comment | New version |
2017-02 | RAN1#88 | R1-1703641 |
|
|
| Skeleton TR based on TR 38.900 v14.2.0, adding new changes (from R1-1701231, R1-1701406, R1-1701410, R1-1701411, R1-1701412, R1-1701413, R1-1701414, R1-1701416, R1-1701417 and R1-1701425) to have a TR applicable for the frequency range 0.5 to 100 GHz | 0.0.1 |
2017-02 | RAN1#88 | R1-1704145 |
|
|
| Adding new agreements (from R1-1702701, R1-1703635, R1-1703873, R1-1703358, R1-1702703, R1-1703637, R1-1703354, R1-1703458, R1-1703647, R1-1701936) and reflecting comments from RAN1 e-mail discussions [88-17]. | 0.1.0 |
2017-02 | RAN#75 | RP-170394 |
|
|
| Submitted to RAN for information | 1.0.0 |
2017-03 | RAN#75 | RP-170737 |
|
|
| Add TR number further to the approval of revised SID in RP-170379. Submitted to RAN for one step approval | 1.0.1 |
2017-03 | RAN#75 |
|
|
|
| MCC clean-up – Further to RAN#75 decision, TR goes under change control as Rel-14 | 14.0.0 |
2017-06 | RAN#76 | RP-171208 | 0001 | - | F | Correction on site specific effective height for TR38.901 | 14.1.0 |
2017-06 | RAN#76 | RP-171208 | 0003 | - | F | Correction for LOS probability | 14.1.0 |
2017-06 | RAN#76 | RP-171208 | 0004 | - | F | Correction for scaling factor for ZOD generation | 14.1.0 |
2017-06 | RAN#76 | RP-171208 | 0005 | - | F | Correction for modelling of intra-cluster angular and delay spreads | 14.1.0 |
2017-06 | RAN#76 | RP-171208 | 0007 | - | F | TR38.901_CR_LCS_Radiation_Field_Notation | 14.1.0 |
2017-06 | RAN#76 | RP-171208 | 0008 | - | F | TR38.901_CR_Mean_angle_definition | 14.1.0 |
2017-06 | RAN#76 | RP-171208 | 0009 | - | F | TR38.901_CR_O2I_penetration_loss | 14.1.0 |
2017-06 | RAN#76 | RP-171208 | 0010 | 1 | F | TR38.901_CR_Correction_on_Rural_LSP | 14.1.0 |
2017-06 | RAN#76 | RP-171208 | 0011 | 1 | F | Clarification for spatial consistency procedure | 14.1.0 |
2017-06 | RAN#76 | RP-171208 | 0013 | - | F | TR38.901_CR_Scenarios_in_Simulation_Assumptions | 14.1.0 |
2017-06 | RAN#76 | RP-171208 | 0014 | - | F | TR38.901_CR_TDL_Spatial_Filter | 14.1.0 |
2017-06 | RAN#76 | RP-171208 | 0015 | - | F | Correction of spatial-consistency UE mobility modelling Procedure A | 14.1.0 |
2017-07 |
|
|
|
|
| MCC: UMa LOS probability in Table 7.4-2 is made fully visible | 14.1.1 |
2017-09 | RAN#77 | RP-171648 | 0016 | - | F | Correction on the formula of Annex A | 14.2.0 |
2017-09 | RAN#77 | RP-171648 | 0018 | 2 | F | Correction on cross polarization power ratios in TR 38.901 | 14.2.0 |
2017-09 | RAN#77 | RP-171648 | 0019 | 1 | F | TR38.901_CR_Spatially-consistent_UT_modelling | 14.2.0 |
2017-09 | RAN#77 | RP-171648 | 0020 | 1 | F | TR38.901_CR_Calibraiton_Results | 14.2.0 |
2017-12 | RAN#78 | RP-172687 | 0022 | - | F | Correction on 3D-InH channel model in TR 38.901 | 14.3.0 |
2018-06 | SA#80 | - | - | - | - | Update to Rel-15 version (MCC) | 15.0.0 |
2019-09 | RAN#85 | RP-191944 | 0023 | - | B | Addition of indoor industrial channel model – version created in error – withdrawn | 15.1.0 |
2019-10 | RAN#85 | RP-191944 | 0023 | - | B | Addition of indoor industrial channel model – creation of rel-16 report | 16.0.0 |
2019-12 | RAN#86 | RP-192629 | 0024 | - | F | CR to TR 38.901 for remaining open issues in IIOT channel modelling | 16.1.0 |
2022-03 | SA#95-e |
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| Update to Rel-17 version (MCC) | 17.0.0 |
2023-12 | RAN#102 | RP-233727 | 0025 | - | F | CR correcting scaling of angles for CDL model and a figure reference | 17.1.0 |
2024-03 | SA#103 | - | - | - | - | Update to Rel-18 version (MCC) | 18.0.0 |
2025-06 | RAN#108 | RP-251566 | 0026 | - | B | Introduction of Rel-19 7-24 GHz channel model enhancements | 19.0.0 |
2025-06 | RAN#108 | RP-251567 | 0027 | - | B | CR to introduce channel model for ISAC | 19.0.0 |
2025-09 | RAN#109 | RP-252622 | 0028 | - | F | Correction of channel modeling enhancements for 7 - 24 GHz | 19.1.0 |
2025-09 | RAN#109 | RP-252623 | 0029 | - | F | CR to incorporate new agreements on ISAC CM | 19.1.0 |